Papers with RSA
Pragmatically Informative Image Captioning with Character-Level Inference (N18-2)
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| Challenge: | a neural image captioner and a Rational Speech Acts (RSA) model are pragmatically informative . previous attempts to combine RSA with neural image-captioning require an inference which normalizes over the entire set of possible utterances. |
| Approach: | They propose a neural image captioner with a Rational Speech Acts model to make it pragmatically informative. |
| Outcome: | The proposed system outperforms a non-pragmatic baseline and word-level RSA captioner on a word-based model. |
Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)
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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong
| Challenge: | Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited. |
| Approach: | They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning . |
| Outcome: | The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster. |
Sequence Parallelism: Long Sequence Training from System Perspective (2023.acl-long)
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| Challenge: | Existing work on memory-efficient parallelisms to reduce time and space complexity focuses on reducing time and complexity from system perspective. |
| Approach: | They propose a memory-efficient parallelism to reduce time and space complexity . they split input sequence into multiple chunks and feed each chunk into GPU . |
| Outcome: | The proposed approach is compatible with most existing parallelisms and makes 4D parallelismal possible. |
Do Large Language Models Mirror Cognitive Language Processing? (2025.coling-main)
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| Challenge: | Large language models have demonstrated remarkable abilities in text comprehension and logical reasoning. |
| Approach: | They employ Representational Similarity Analysis to measure alignment between 23 LLMs and fMRI signals of the brain. |
| Outcome: | The results show that training strategies affect the LLM-brain alignment. |
Correlating Neural and Symbolic Representations of Language (P19-1)
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| Challenge: | a popular technique for analyzing neural representations involves predicting information of interest from the activation patterns. |
| Approach: | They propose to use Representational Similarity Analysis and Tree Kernels to quantify how strongly activation patterns correspond to symbolic representations. |
| Outcome: | The proposed methods show that they exhibit the expected pattern of results on a synthetic language. |
RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework (2024.emnlp-main)
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| Challenge: | RSA-Control is a training-free controllable text generation framework . existing studies rely on fine-tuning pre-trained language models . external components could hurt coherence and accuracy of the model . |
| Approach: | They propose a training-free controllable text generation framework grounded in pragmatics that directs the generation process by recursively reasoning between imaginary speakers and listeners. |
| Outcome: | The proposed framework achieves strong attribute control while maintaining fluency and content consistency. |
Picking BERT’s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis (2020.coling-main)
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| Challenge: | Contextualized word embeddings can incorporate contextual information, whereas other embeddables cannot. |
| Approach: | They propose an approach to address this question using Representational Similarity Analysis (RSA) they investigate whether verb embeddings encode verb’s subject, pronoun embedds antecedent and full-sentence representations encode sentence’s head word . |
| Outcome: | The proposed approach can adjudicate between hypotheses about which aspects of context are encoded in representations of language. |
Are explicit belief representations necessary? A comparison between Large Language Models and Bayesian probabilistic models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have indirect pragmatic capabilities, but their performance on Theory of Mind tasks is mixed. |
| Approach: | They propose to use Bayesian probabilistic model to make inferences about others' beliefs to predict human belief inference. |
| Outcome: | The proposed model outperforms the Rational Speech Act (RSA) framework in predicting human belief inferences, even though it does not explicitly encode belief representations. |
Higher-order Comparisons of Sentence Encoder Representations (D19-1)
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| Challenge: | a technique developed by neuroscientists compares activity patterns of different measurement modalities . a recent study examined the correspondence between popular pretrained language encoders and human processing difficulty . |
| Approach: | They employ a technique to compare activity patterns of different measurement modalities . they establish a correspondence between widely-employed pretrained language encoders and human processing difficulty . |
| Outcome: | The proposed technique can be used to compare representational geometries of neural models . it does not require large training samples and is not prone to overfitting, authors say . |
Beyond Value Benchmarks: Measuring Value-Structure Alignment in Large Language Models via Symmetric Q-Sorts (2026.acl-long)
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| Challenge: | Existing evaluations of Large Language Models (LLMs) focus on item-level behavioral metrics without capturing how models prioritize competing values as a whole. |
| Approach: | They propose a symmetric human-LLM evaluation framework to measure value-structure alignment . they evaluate 12 LLMs across four model families via 240 replicated Q-sorts . |
| Outcome: | The proposed framework measures value-structure alignment across four model families. |
RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios (2026.findings-acl)
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Yibo Zhang, Kaiwen Luo, Liang Lin, Shilinlu Yan, Jin Wang, Yaoqi Guo, Yitian Chen, Yalan Qin, Zhenhong Zhou, Kun Wang, Li Sun
| Challenge: | Existing evaluations rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics that characterize authentic physical environments. |
| Approach: | They propose a robustness benchmark to stress-test Audio Large Models (ALLMs) using high-fidelity auditory scene simulations. |
| Outcome: | The proposed model performs well on a wide range of tasks, including automatic speech recognition, speech translation, and audio-based reasoning. |
On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts (2025.emnlp-main)
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| Challenge: | Language models (LMs) are increasingly used as conversational agents because of their pragmatic reasoning abilities. |
| Approach: | They propose an evaluation framework derived from *Wavelength*, a popular communication game where a speaker and a listener communicate about a broad range of concepts in a granular manner. |
| Outcome: | The proposed evaluation framework outperforms direct and Chain-of-Thought (CoT) prompting on language comprehension and language production tasks. |
(RSA)²: A Rhetorical-Strategy-Aware Rational Speech Act Framework for Figurative Language Understanding (2025.acl-long)
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| Challenge: | Existing implementations of the Rational Speech Act (RSA) framework do not account for figurative expressions or require modeling the implicit motivations behind using figurativ language in a setting-specific way. |
| Approach: | They propose a framework which models figurative language use by considering a speaker's employed rhetorical strategy and a computational model which incorporates rhetorical strategies to support non-literal interpretation. |
| Outcome: | The proposed framework enables human-compatible interpretations of non-literal utterances without modeling speaker's motivations for being non-lative. |
Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog (2025.emnlp-main)
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| Challenge: | Existing extensions of Rational Speech Act face challenges in scaling to multi-turn, collaborative scenarios. |
| Approach: | They propose a Rational Speech Act extension that optimizes a gain function adapted from rate-distortion theory to model multi-turn dialog by optimizing a model gain . they demonstrate the effectiveness of CRSA on referential games and template-based doctor–patient dialogs in the medical domain. |
| Outcome: | The proposed model yields more consistent, interpretable, and collaborative behavior than baselines, paving the way for more pragmatic and socially aware language agents. |
Foresight Optimization for Strategic Reasoning in Large Language Models (2026.acl-long)
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Jessie Wang, Jiawen Duan, Jian Wang, Kaitao Song, Chunpu Xu, Johnny K. W. Ho, YU Fenggang, Johan F. Hoorn, Wenjie Li
| Challenge: | Existing reasoning enhancement methods do not capture foresight in LLMs. |
| Approach: | They propose to integrate opponent modeling principles into policy optimization to enhance strategic reasoning in LLMs by integrating opponent modeling into policy. |
| Outcome: | The proposed method outperforms existing reasoning-based LLMs in out-of-domain scenarios and shows that it significantly enhances strategic reasoning across LLM of varying sizes and origins. |