Papers with cognitive psychology
Predicting Difficulty and Discrimination of Natural Language Questions (2022.acl-short)
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| Challenge: | Item Response Theory (IRT) has been used to numerically characterize question difficulty and discrimination for human subjects in domains including cognitive psychology and education. |
| Approach: | They explore the relationship between difficulty and discrimination in question-answering contexts by using IRT to characterize item difficulty and item discrimination. |
| Outcome: | The proposed models can predict difficulty and discrimination parameters for new questions and explain them with features of questions, answers, and associated contexts. |
Conceptual structure coheres in human cognition but not in large language models (2023.emnlp-main)
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| Challenge: | Neural network models of language have long been used to develop hypotheses about conceptual representation in the mind and brain. |
| Approach: | They propose to use three techniques borrowed from cognitive psychology to estimate lexical-semantic structure in humans and a large language model. |
| Outcome: | The proposed models show that human-like models can estimate lexical-semantic structure robustly to cultural, language, and method of estimation. |
ALIGN: Word Association Learning for Cultural Alignment in Large Language Models (2026.acl-long)
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| Challenge: | Large language models exhibit cultural bias from over-represented viewpoints in training data, yet cultural alignment remains a challenge due to limited cultural knowledge and a lack of exploration into effective learning approaches. |
| Approach: | They propose a cost-efficient method for fine-tuning large language models on native speakers’ word-association norms and a preference optimization method to improve cultural alignment. |
| Outcome: | The proposed model trains Llama-3.1-8B and Qwen-2.5-7B on native speakers’ word-association norms and shows that such associations capture cultural knowledge. |
Creating Suspenseful Stories: Iterative Planning with Large Language Models (2024.eacl-long)
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| Challenge: | Automated story generation has been a challenge in NLP for many years. |
| Approach: | They propose an iterative-prompting-based method that is grounded in two theoretical foundations of story suspense from cognitive psychology and narratology. |
| Outcome: | The proposed method works in a fully zero-shot manner and does not rely on any supervised story corpora. |
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction (2023.findings-emnlp)
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| Challenge: | Existing studies have focused on word analogies, but they neglect structures that underpin analogical reasoning. |
| Approach: | They propose a task to abduct structures that form an analogy between two systems to evaluate their analogical reasoning abilities. |
| Outcome: | The proposed task is based on 400 scientific analogies from 13 different fields and is compared with a standard SCAR benchmark. |
Unveiling Confirmation Bias in Chain-of-Thought Reasoning (2025.findings-acl)
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| Challenge: | Chain-of-thought (CoT) prompting has been widely adopted to enhance the reasoning capabilities of large language models (LLMs). |
| Approach: | They propose to examine how internal beliefs affect reasoning generation and reasoning-guided answer prediction in CoT by decomposing CoT into a two-stage process. |
| Outcome: | The proposed model beliefs affect reasoning generation and reasoning-guided answer prediction in CoT, and the results provide strong evidence of confirmation bias in LLMs. |
Lifelong Knowledge-Enriched Social Event Representation Learning (2021.eacl-main)
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| Challenge: | Existing approaches to represent social events and situations fail to consider pragmatic aspects . a conceptual framework for lifelong language learning integrates commonsense knowledge with lifelong learning. |
| Approach: | They propose a representation learning framework that integrates pragmatic aspects with lifelong language learning. |
| Outcome: | The proposed model integrates commonsense knowledge with lifelong language learning to produce richer representations of social events. |
GLUCOSE: GeneraLized and COntextualized Story Explanations (2020.emnlp-main)
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Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll
| Challenge: | Existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE’s rich inferential content. |
| Approach: | They propose a platform for crowdsourcing GLUCOSE data at scale that uses semi-structured templates to elicit causal explanations. |
| Outcome: | The proposed model can be trained on human-readable stories and build similar models on unseen stories. |
Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning (2024.acl-long)
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| Challenge: | Recent advances in the domain of large language models (LLMs) have showcased their capability in executing deductive reasoning tasks. |
| Approach: | They examine inferential strategies employed by large language models through a detailed evaluation of their responses to propositional logic problems. |
| Outcome: | The proposed model shows that it displays reasoning patterns similar to humans, including strategies like supposition following or chain construction. |
Where Fact Ends and Fairness Begins: Redefining AI Bias Evaluation through Cognitive Biases (2025.findings-emnlp)
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| Challenge: | Existing benchmarks conflate factual correctness and normative fairness . a model may generate responses that are factually accurate but socially unfair . |
| Approach: | They propose a benchmark to examine the boundary between fact and fair . they draw on representativeness bias, attribution bias and ingroup–outgroup bias to explain why models often misalign fact and faireness. |
| Outcome: | The proposed model is based on ten frontier models and is available on github . it is compared with a standard model that generates people of color in Nazi-era uniforms . |
Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition (2025.coling-main)
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| Challenge: | Theory of Mind (ToM) is the ability to attribute and infer the mental states of others. |
| Approach: | They propose an LLM-based inference algorithm that improves model performance on complex ToM tasks by simulating user perspectives. |
| Outcome: | The proposed algorithm improves model performance on complex ToM tasks while requiring minimal prompt tuning across tasks and no additional model training. |
AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs (2026.acl-long)
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| Challenge: | Evaluating multimodal large language models (MLLMs) is becoming increasingly expensive as benchmarks grow in scale and cross-modality complexity. |
| Approach: | They propose an adaptive evaluation framework for efficient benchmarking that treats evaluation as an interview-like process by keeping a hypothesized ability structure of the evaluated model and actively selecting the informative questions. |
| Outcome: | Experiments on four representative multimodal benchmarks show that **A2-Judger significantly improves sample efficiency while maintaining reliable evaluation results. |
A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences (2024.emnlp-main)
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| Challenge: | syllogistic reasoning is a deductive reasoning skill that is crucial in everyday problem-solving and decision-making experiences. |
| Approach: | They propose to study the reasoning abilities of Large Language Models (LLMs) they propose to use supervised fine-tuning and chain-of-thought reasoning to investigate their results. |
| Outcome: | The proposed models exhibit reasoning biases, avoid answering that no conclusion follows, align with human difficulties, and struggle with multi-step reasoning. |
Can Language Models Serve as Analogy Annotators? (2025.findings-acl)
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| Challenge: | Conceptual abstraction and analogy-making are crucial for human learning, reasoning, and adapting to unfamiliar domains. |
| Approach: | They propose a multi-stage progressive reasoning prompt framework A3E which is based on the structure mapping theory from cognitive psychology and efficiently annotates candidate story pairs across six fine-grained categories. |
| Outcome: | The proposed framework achieves an average performance gain of + 73% across a range of prompting baselines and base LLMs. |
Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning (2026.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their autoregressive generation paradigm makes it computationally prohibitive to explore diverse reasoning paths. |
| Approach: | They propose a framework that combines diffusion-based generation with autoregressive evaluation to efficiently generate diverse intermediate reasoning thoughts and employ LLMs as evaluators to assess and select candidates based on their plausibility and correctness. |
| Outcome: | The proposed framework improves inference efficiency while maintaining competitive or superior reasoning accuracy. |
CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards (2025.emnlp-main)
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| Challenge: | Existing approaches to role-playing language models rely on prompt engineering or supervised fine-tuning to emulate character behaviors but neglect the underlying cognitive mechanisms driving these behaviors. |
| Approach: | They propose a novel RPLA adopting a cognize-then-respond reasoning paradigm that leverages dual cognition for more contextually grounded and psychologically coherent responses. |
| Outcome: | The proposed RPLA outperforms baselines and generalizes effectively across diverse role-playing tasks. |
Injecting Context via Situation Working Memory for Logical Reasoning with LLMs (2026.acl-long)
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| Challenge: | Recent advances in large language models have improved logical reasoning by injecting formal logic or explicit structured representations. |
| Approach: | a cognitively inspired method is proposed to help LLMs construct a mental representation of events . SituW builds a situation representation by decomposing text along these five dimensions . it also guides LLM inference with this evolving state . |
| Outcome: | a cognitively inspired method improves accuracy and predictability in large language models . SituW builds a mental representation by decomposing text along these dimensions . |
D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory (2026.findings-acl)
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| Challenge: | Conventional interactive algorithms have predominantly treated memory as a contextual element, neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval. |
| Approach: | They propose a multi-turn dialogue dataset with Personalized Contextual Memory to facilitate advanced research on personalized memory processing. |
| Outcome: | The proposed datasets provide a comprehensive benchmark to facilitate advanced research on personalized memory processing. |