arXiv_CL
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How Post-Training Influences Verbalized Algorithmic Computation in Language Models
Abstract
Post-training improves language-model reasoning, but how it changes the use of internal information and verbalized chain of thought (CoT) remains unclear. We study successive SFT, DPO, and RLVR checkpoints of Olmo-3-7B-Instruct on multi-digit addition, using carry values as known intermediate state. Probes recover carry information from internal activations with similar accuracy in SFT and RLVR, although manipulating this information does not reliably control model outputs. In contrast, the usefulness of verbalized reasoning differs: CoTs produced by RLVR yield higher accuracy across receiving checkpoints than those produced by SFT or DPO. We also show that carry-related statements receive higher importance scores after RLVR than matched random controls. DPO shows similar sensitivity to carry-related statements but without comparable accuracy gains. These results suggest that post-training changes the usefulness of written reasoning and sensitivity to its content in meaningful ways, although greater sensitivity does not necessarily coincide with better problem performance.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13552
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Sanity Checks for Attention Surgery: Null Models and Matched Interventions for Representation-Motivated Design
Abstract
Transformer architectural modifications frequently measure a statistic on internal representations, observe that it is large, remove it architecturally, and report an improvement. Interpreting that sequence requires evaluating whether the statistic exceeds its noise floor, whether it survives a geometric null, and whether the training gain requires the specific direction removed. We formulate these requirements as three reusable checks and apply them to self-attention removal, which deletes the component of an attention head's output aligned with its own value vector. Across nine multi-head pretrained models (5,408 heads, 124M to 6.9B parameters), an earlier-position causal null accounts for 48% to 63% of the head-value cosine alignment, with no monotone scale trend. Under grouped-query attention, this alignment is specific to each head's own key-value group and reverses across groups (+0.24 to +0.27 within-group, -0.11 to -0.19 across-group). Split-half reliability demonstrates that the per-head intervention effect is resolvable in GPT-2 ($r_\Delta = 0.80$) but attenuated in Pythia-160M (0.42) and Pythia-410M (0.53), whereas structural self-cosine reliability is near-perfect in all three ($r_{\text{stat}} > 0.99$); which statistic predicts its own intervention is model-dependent. In a pre-registered 3-arm by 8-seed paired factorial at $4.0 \times 10^8$ tokens per run (399,900,672 tokens), a fixed arbitrary rank-one direction does not differ from baseline (+0.0011 nats, $p = 0.197$), while self-directed removal lowers validation loss (-0.0029 nats, Holm-corrected $p = 0.0023$) and outperforms the fixed arbitrary control directly (-0.0040 nats, $p = 0.0018$). At 50.9M scale the training benefit requires the own-value direction rather than a fixed arbitrary one, but a large cosine alignment alone does not establish redundancy in frozen models.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13543
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Framing-Induced Vulnerability in Large Language Models: Evidence from Chinese News Headlines
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Large language models increasingly evaluate, route, and moderate news, so a model's judgment of a headline has become a gatekeeping decision. We ask whether that judgment follows the fact being reported or the way the fact is framed. We combine an ecological audit of 63,926 Chinese news headlines with a controlled experiment. The experiment covers 30 real events, 30 synthetic events, and 29 natural headline pairs, tested on four Chinese models. Changing only the headline frame shifted both perceived misleadingness and event severity, and the direction and ordering of the shifts were the same for DeepSeek, MiMo, Dots, and LongCat. Emotionally intensified headlines raised severity ratings by 0.65 to 0.88 points, whereas positively reframed headlines lowered them by 0.34 to 0.52 points. Supplying the article body reduced this severity bias by 54 to 80 percent, and all four reductions were significant at p < .01. A small human reference study showed the same direction of effect. Headline-only LLM evaluation therefore carries a measurable robustness gap, and framing sensitivity belongs among the reliability properties that deployment and governance check.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13531
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EJAI: Judgment-Preserving Civic AI for Data Center Environmental Justice
2026-11-10 03:50:04Evan Pa\v{c}es-Wiles, Bryen Alvarez, Sara Gulabani, Akshara Kolipaka, Snehalkumar 'Neil' S. GaikwadAbstract
Communities in the United States and around the world are contesting data centers, the physical infrastructure of AI. These disputes pose environmental justice issues: residents must weigh unevenly distributed burdens using evidence scattered across metrics, corporate claims, policy documents, and public testimony. A formative study with 10 users showed that dashboards and single-agent AI can settle questions before people examine that evidence. Here we present EJAI, a source-grounded system that links an interactive map, retrieved evidence, missing-data statements, and two stakeholder personas that allow people to inspect evidence and form their own policy recommendations. In a small functional benchmark of 21 queries, retrieved evidence supported 98.4% of factual claims and the system acknowledged missing evidence in 32 of 33 cases. Persona alignment carried a grounding cost: the Corporate Executive omitted some evidence that conflicted with its assigned priorities. In a within-subjects study, 15 participants acting as residents prepared for a simulated town hall. Some checked cited AI claims while treating map metrics as official, some made conditional recommendations with safeguards such as water-use disclosure, and some found the personas simplified or biased. Our results suggest that civic AI can support preparation for public reasoning when it makes evidence, gaps, and disagreement open to inspection, and that corpus imbalance and two-sided personas remain open justice concerns.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13525
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Differentiable Filtering for Inertial Odometry
Abstract
Accurate inertial odometry is critical for autonomous systems but suffers from unbounded drift caused by the integration of sensor noise and biases. In approaches such as TLIO, neural pseudo-measurements are trained independently of their downstream filter. This paper introduces Differentiable Filtering for Inertial Odometry (DFIO), a learning framework that embeds the update stages of an error-state Kalman filter directly into a computational graph. Following standalone pre-training, DFIO jointly optimizes the displacement and covariance output heads using posterior-position supervision through differentiable measurement updates. The proposed system is validated on public pedestrian motion datasets featuring both calibrated and uncalibrated inertial measurements. Experimental results on a pedestrian dataset demonstrate that DFIO reduces mean absolute and one-second relative translation errors by 13.2% and 39.6% relative to the TLIO baseline, and by 7.7% and 17.4% relative to its pre-trained counterpart. Furthermore, evaluations on uncalibrated smartphone inertial data indicate that the proposed method effectively mitigates translational drift and maintains trajectory alignment under highly noisy sensor conditions. Source code will be made publicly available.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13517
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Mobile AI Factory: Building Specialized Language Models for Your Mobile Device
2026-11-10 03:29:29Evan Li, Krishna Deshpande, Wen Cheng, Shyamnath GollakotaAbstract
Small language models (SLMs) running on mobile devices can keep user data private and work without connectivity. But they perform well only when fine-tuned for the target task and sized for the target device, a process that today requires repeated expert tuning. We present {\it Mobile AI Factory}, an agentic system that builds a fine-tuned, quantized SLM for a given task and phone. Given a task specification and a target phone, an orchestrator LLM chooses whether to tune hyperparameters, mine real data, or generate synthetic examples, while fixed rules roll back changes that hurt accuracy. The factory searches from small to large models across base models and quantizations, adapts its accuracy goal as it searches, and uses real measurements of the target phone across inference engines and backends. We evaluate Mobile AI Factory on ten tasks and four Android phones. On an unseen testset, its on-device models outperform two five-shot frontier models on five tasks, by up to 0.34, and match them on three more. Its search comes within 0.03 of exhaustively training all 27 model variants on an entity extraction task, while using 19$\times$ less GPU time. Phone measurements also matter: the best configuration changes across phones. Without phone measurements, Claude Opus 5.5, given the full hardware and model specifications, misses the app's requirements in 25\% of easy and moderate cases and in 60\% of hard ones. Code is available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13509
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Evaluating Compositional Generalization in Transformers: The Role of Composition Equivalence and Module Coverage
2026-11-10 03:09:02Purva Pruthi, Andrew Yuan, Alexander D'Amour, David JensenAbstract
Compositional generalization is an important form of out-of-distribution generalization, defined as the ability to train on some combinations of modules (e.g., functions such as sort, filter) and then generalize to unseen combinations. Despite substantial empirical work evaluating compositional generalization in transformers, findings have been inconsistent because evaluations were often conducted on a limited set of train-test splits, defined in an ad hoc manner. Through a systematic evaluation, we identify two key aspects of the train-test distribution that strongly affect performance: composition equivalence and module coverage. We show that the apparent performance of direct models (trained only on final outputs) results primarily from exploiting composition equivalences, meaning different sequences of modules that reduce to identical end-to-end functions. When benchmarks eliminate these equivalences, performance drops significantly, showing their inability to generalize to compositions of known modules that produce novel end-to-end functions. We further demonstrate that performance degrades monotonically as module coverage shifts increase (when modules appear in different contexts between train and test), affecting both direct and step-by-step models (trained on intermediate and final outputs). These findings have concrete implications for benchmark design: benchmarks that include compositional equivalences can lead to significant overestimation of the generalization abilities of direct models, and benchmarks that include both module coverage violations and compositional equivalences can exacerbate shortcut learning. As a case study, we demonstrate the role of compositional equivalence on the generalization performance of the pre-trained model Llama3-8B on the code-output prediction task in the coding benchmark LiveCodeBench.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13498
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Off-target posterior mass: A training-free diagnostic for fine-tuning spoken language diarization probes
2026-11-10 01:15:31Zhuoyan Tao, Aditya Kommineni, Shrikanth NarayananAbstract
Spoken Language Diarization (SLD) models provide frame-level language information of a speech recording. SLD modeling pipeline includes feature extraction from pre-trained speech models followed by clustering. Fine-tuning pre-trained speech models has shown performance gains for SLD with its magnitude being dataset dependent. Off-target Posterior Mass (OTPM), computed based on posterior language identification probabilities is proposed as a diagnostic metric to forecast performance gains from SLD fine-tuning. OTPM obtains positive correlation with probe gains, 0.56 on unseen synthetic bilingual data and 0.23 on real-world corpora. These results indicate promising potential of OTPM as a diagnostic metric for SLD.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13430
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BioMem: Agentic Memory for Biomedical LLM Agents
2026-11-10 01:09:11Mingchen Li, Yongkang Xiao, Rumeng Li, Rui Zhang, Hong YuAbstract
Large language models (LLMs) have demonstrated considerable potential in biomedical applications, while agentic memory systems have emerged as a promising approach to preserving and utilizing information across tasks and interactions. However, a fundamental question remains: how effectively and reliably do these systems retain, retrieve, update, and utilize biomedical information? In this study, we systematically investigate agentic memory in the biomedical domain, focusing on task effectiveness, retrieval quality, information fidelity, and long-term robustness. We evaluate five memory approaches across four LLMs in three representative settings: biomedical knowledge reasoning, clinical prediction, and long-term conversational memory. Our analyses reveal how memory construction, evidence relevance, and conversational history shape downstream biomedical performance, highlighting the distinct challenges of retrieving relevant information, preserving its fidelity, and effectively utilizing it for reasoning. Building on these findings, we introduce Hybrid Memory Retrieval and Utilization (HMRU), a training-free framework that jointly enhances evidence accessibility and task-aware utilization to improve biomedical reasoning and long-term conversational memory. HMRU achieves an average relative improvement of 8.6% across biomedical question-answering and conversational memory benchmarks, with additional gains in selected clinical prediction tasks. It further reduces conversational answer errors by 24.9% and closes 48.0% of the performance gap to full-context inference. Our findings provide a systematic characterization of biomedical agentic memory and practical insights into developing more effective and reliable memory-augmented LLM systems.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13426
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Orthographic depth at two scales, with English also matched for lexicon size
Abstract
A reader can struggle to read in only one of their languages, and one proposed reason is how predictably spelling maps to sound. We measure that predictability as conditional entropy, the pronunciation uncertainty left once the spelling is seen, at two grain sizes (unit sizes): the alignment unit, an aligned spelling-sound chunk, and the syllable, bounded in the pronunciation. We analyse word lists in twelve alphabetic orthographies and report five in full, with English pronunciations from a dictionary and the rest from the synthesizer eSpeak-ng, length marks removed. Among the five, English has the most uncertainty and Finnish the least at both grains. Across all twelve, Portuguese and Danish exceed English per alignment unit, and German and Italian order differently by grain and transcription. These orders hold when English is transcribed by eSpeak-ng with u allowed to spell /j/ plus a vowel. Uncertainty per unit has no shared direction as units grow: it falls in Spanish and, less securely, German, is level in Finnish, depends on transcription in Italian and on the word list in English, and rises in Norwegian and Swedish. Summed uncertainty per word falls in all twelve, but that score is handed the pronunciation's boundaries and mixes context with in-sample memorisation, so it is no evidence of what a reader computes. An exact identity states when discarded spelling boundaries could reverse that fall. A uniform 20,000-word English sample holds 35% of the dictionary's syllable spellings and has syllable entropy 0.098 bits lower, so lexicons must be matched in size. In an exploratory check over eleven languages, syllable-scale entropy tracked first-year reading accuracy (rho = -0.74, falling to -0.25 if English is given the lowest entropy instead), without separating the syllable scale from the alignment unit. These estimates describe sampled writing systems, not readers.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13400
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Mapping Instructors' Attitudes Towards Cheating with AI: An Exploratory Analysis Using Forum Analysis
2026-11-09 23:39:11Iman Mohammadi, Ali Keramati, Sina RismanchianAbstract
In teaching and learning environments, cheating and the violation of academic integrity have always been one of the challenges, involving different levels of concerns such as administrative, personal knowledge, learning, and equity. The introduction of large language models has brought cheating in dialogue to the forefront, given the apparent capabilities of these models to complete students' academic assignments. This study aims to leverage recent dialogues among university professors about cheating with Generative AI to better understand their beliefs about academic honesty. In doing so, we analyze a corpus of 2.01 million posts (threads and comments) in the r/Professors subreddit discussing cheating after the release of ChatGPT, providing a better understanding of teachers' motivations to discourage cheating and the strategies they use to do so. We synthesize the why and how of addressing cheating to identify caveats about the future of AI and academic integration.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13366
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Understanding Students' Rationales for AI-Mediated Cheating: An Instructor Perspective from Online Forums Analysis
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The extensive adoption of AI has precipitated a crisis in academic integrity. While studies attempt to quantify cheating, less is known about the motivations instructors believe students have for using AI in prohibited ways. We analyze 2.01 million posts on r/Professors to understand how instructors perceive students' justifications for AI-mediated cheating. Our qualitative analyses reveal four primary themes: (1) Academic System Gaming, (2) Digital-Academic Literacy Gap, (3) Performance Anxiety, and (4) Normalization of Dishonesty.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13358
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Measuring Definition Sensitivity in LLM Annotation of Social Science Concepts
2026-11-09 23:04:13Bogdan Mamaev, Qizhou Wang, Christopher Leckie, Matteo VerganiAbstract
In social science research, text annotation is a fundamental task that is traditionally a costly, hard-to-scale manual process. Researchers have used large language models (LLMs) to perform annotation by writing a definition of a concept into a prompt - a rule that determines whether a label should be attributed to a text depending on its content. However, concepts such as hate speech, fake news, and terrorism typically have several theoretically defensible competing definitions. It is also unknown whether the model actually applied the definition to provide the label or the label was based on the model's pretrained notion of the concept. To test this, we have the model annotate the same texts twice, each time with a different definition. We call the disagreement between the two sets of annotations definition sensitivity. As a control, we paraphrase one definition but leave its requirements unchanged - the disagreement between the annotations with the two wordings becomes the paraphrase floor. Across six concepts and three LLMs, a swap of competing definitions always changes more annotations, on average, than a paraphrase. This shows that the annotations depend on the prompted definition, so any set of annotations should be interpreted together with the definition that produced it. The LLM annotator's definition sensitivity and its paraphrase floor should both be reported in such work.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13339
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MicroLens: Read, Trace, and Rewire the Residual Stream with Two Lenses
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Lenses that decode hidden states into words are a popular way to look inside language models, but a heatmap of decoded words only shows what a vector reads as and cannot distinguish whether, or how much, the model uses the information. We present an interactive viewer that puts reading, tracing and editing on the same layers-by-tokens grid of the residual stream. In the viewer, every cell can be decoded with two lenses, the logit lens and the Jacobian lens (J-lens), traced back to the tokens it drew from, and edited, with the rest of the model rerun on the spot. We demonstrate three usage scenarios on Gemma-3-4B-it, backed by sweeps over a few hundred prompts, to show what this tool can discover. First, the two lenses give different directions to write with, each suiting a different edit: the logit lens's word direction sets what a token outputs, while the J-lens's direction changes what later tokens read. Second, in two-hop trivia questions (the capital of the state or country containing a given city), the state or country is readable at the city token from as early as layer 7, and swapping it there can redirect the answer 92-98% of the time; however, at the answer position, the state or country information is not used. Third, when a chat model answers questions with a step-by-step reasoning prompt, the correct answer is often already the top word in the residual stream before any reasoning is written: for 89-92% of retrieval questions and 39-63% of multi-hop and serial ones, far above their respective distractors (at most 5%). However, ordering puzzles are an exception, as the model works them out only once it writes its final reasoning steps. Furthermore, for answers that are visible in the residual stream, the J-lens reads them a layer or two earlier than the logit lens across the different types of question.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13322
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Decode Token Parallelism: Memory-Efficient Distributed Attention Parallelism for Scalable LLM Decoding
2026-11-09 22:01:46Susav Shrestha, Brad Settlemyer, Narasimha ReddyAbstract
Serving large language models at high concurrency with long contexts requires KV-cache capacity and attention bandwidth that grow with the decode pool. Existing parallelization strategies trade one against the other: data parallelism replicates model weights, limiting KV capacity; tensor parallelism stops scaling attention at the model's KV-head count and pushes dense collectives across nodes; and decode context parallelism (DCP) recovers capacity by sharding the sequence but adds cross-node aggregation of partial attention results. We observe that during decode, dense layers and attention scale differently: dense layers amortize weight reads across the batch and run efficiently within a single NVLink domain, whereas attention cost grows with every active request's KV cache. We present Decode Token Parallelism (TKNP), a decode-pool architecture that decouples weight placement from attention ownership. TKNP distributes a single model copy across nodes in balanced layer blocks. The active node holding a block executes its projections and MLP for the full batch using intra-node tensor parallelism, while every node computes attention for its own requests against locally resident KV cache. This extends request-local attention across nodes without replicating weights: only current-token activations cross the network, dense all-reduces remain within NVLink, and attention requires no cross-node merging of partial results. Implemented in vLLM and evaluated on Llama 3.1 405B and Nemotron-4 340B on up to 64 H100 GPUs, TKNP improves completion throughput for long-context, high-concurrency workloads by up to 1.7$\times$ over DCP and by up to 2.06$\times$ over data parallelism at matched GPU count.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13293
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What drives the emergence of symbolic mechanisms in large language models?
2026-11-09 21:36:57Melody Zixuan Li, Taylor WebbAbstract
Recent work has identified a set of emergent symbolic mechanisms that support abstract reasoning in large language models, but it remains unclear what factors drive the emergence of these mechanisms. To address this question, we trained neural networks from scratch on abstract sequence tasks, varying both architectural and data distributional factors, and investigated the effects of these factors on the emergence of symbolic mechanisms. Using representational, attentional, and causal mediation analyses, we first confirmed that transformer-based language models trained from scratch on our task developed symbolic mechanisms. We then found that, across multiple axes of data diversity, greater diversity produced stronger symbolic signatures. Surprisingly, the same signatures emerged in modified transformer architectures and even multilayer perceptrons, indicating that architectural inductive biases are not necessary for the development of symbolic mechanisms. We also observed consistent patterns in an entity-attribute binding task. Together, these findings identify data diversity, not architecture, as the primary driver of emergent symbolic computation in neural networks.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13270
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Domain-Routed Delta Optimization for Multi-Teacher On-Policy Distillation
Abstract
Multi-teacher on-policy distillation assigns each sample a domain teacher but typically updates shared student parameters. We introduce Domain-Routed Delta Optimization (DRD-MOPD), which specifies which parameter components each domain's samples update. The student is the sum of a frozen base and trainable components initialized from expert-minus-base weights. All components participate in every forward pass; four update modes define the correspondence between sample domains and components, each with independent optimizer states. We analyze these update rules and measure retention of the original expert geometry through angles, projection coefficients, and energy. Prior work such as Open-MOPD distills directly from the base model; 97.02% of its released final checkpoint's increment energy lies outside the span of the original expert directions. In comparison, our Own checkpoint after 20 updates retains coefficients near one along each component's original direction and pairwise angles ranging from 88.04 to 92.41 degrees; its exported BF16 student's increment has 4.90% outside energy, versus 10.52% for the sum of saved FP32 components. The same checkpoint achieves a 32.72% macro score across six benchmarks, compared with 32.44% for its initial task-arithmetic merge. These measurements describe geometry and capability at the evaluated training stages and settings.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13249
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Characterising divergence between heuristic and language-model ratings of mobile user interface quality
2026-11-09 21:15:50Md Rejaul Korim Sadi, Golam Mostofa Naeem, Toufiqur Rahman Tasin, Syed Mostofa Moosa, Mahmudul Hasan Emon, Mahmudur Rashid, Ferdus AhmedAbstract
Automated assessment of mobile user interface quality is attractive because manual review does not scale, and large language models are increasingly proposed for the task. Whether their ratings correspond to those produced by established rule-based heuristics has not been characterised at scale. We construct a 15,000-screen corpus from the RICO dataset using six documented filters, score every screen with a literature-grounded heuristic scorer, and collect ratings from three open-weight language models across two input representations, yielding 270,000 ratings. We report linearly weighted Cohen's kappa with bootstrap confidence intervals, offset-corrected kappa, intraclass correlation, and Bland-Altman limits of agreement, together with a chance baseline for the tolerance-band agreement rule. Agreement between the two rating sources is consistently weak: raw kappa falls below 0.15 in seventeen of eighteen model-representation-dimension cells. However, fifteen of eighteen cells are statistically distinguishable from zero, and correcting for a constant offset raises five cells above 0.15, so the divergence is better described as a combination of systematic bias and weak covariation than as absence of relationship. Model ratings track structural properties of the interface, but substantially less strongly than the heuristic does, and more strongly from an untruncated structural element list than from a screenshot. We further show that dimensions elicited in a single generated response are correlated by construction, reaching rho = 0.97 in one condition, which constrains what such designs can measure. We report these results as a characterisation of divergence between two automated raters rather than as evidence that either is correct, and we set out the validation a stronger claim would require.
Abstract (translated)
URL
https://arxiv.org/abs/2609.05423
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Latin American Studies since the 1980s: From Apocalyptic Dreams to Assimilationist Consolidation
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We investigate how Latin American Studies (LAS) has evolved since 1982 in three leading LAS journals. Using network analysis, text mining, and bibliometrics, we trace transformations in disciplinary positioning, authorship, and citation patterns. Our main claim is that LAS has undergone an assimilationist consolidation. The field is now institutionally recognisable, with core LAS journals occupying central positions in disciplinary networks and acting as bridges across fields. Yet this consolidation has reinforced hierarchies privileging Anglophone academic norms. Journals based in Latin America, once central, have been marginalised. The share of Latin American authors has grown, but their profiles converge towards Northern standards.
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URL
https://arxiv.org/abs/2611.13216
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HiCAP: Hierarchical Cultural Adaptation via Personality for Dialogue Generation
2026-11-09 20:30:26Lingzhi Shen, Xiaohao Cai, Yunfei Long, Imran Razzak, Shoaib JameelAbstract
Cultural adaptation in dialogue systems refers to the ability to generate responses that conform to linguistic norms and social expectations across different cultures. However, existing dialogue generation approaches pay limited attention to the role of personality in shaping cultural expression and communication style. We therefore propose HiCAP, a novel framework that integrates personality traits into culturally adaptive dialogue generation. HiCAP employs a personality-conditioned control mechanism that hierarchically modulates response style by first aligning it with the cultural tone and then refining linguistic expression and content according to individual personality attributes. To embed these factors, the model combines supervised fine-tuning and instruction-based tuning, supported by dynamic instruction templates and multi-agent demonstrations to capture diverse combinations of culture and personality. Experiments on CulturalPersonas and CPED show that HiCAP achieves the strongest overall performance across various dialogue generation metrics, demonstrating effective cultural adaptability and personality modelling.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13207
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