| Challenge: | Large language models encode parametric knowledge about world facts but overly rely on it can cause incorrect predictions in context-sensitive NLP tasks. |
| Approach: | They propose to use opinion-based prompts and counterfactual demonstrations to improve LLM faithfulness to contexts. |
| Outcome: | The proposed methods improve faithfulness to contexts using opinion-based prompts and counterfactual demonstrations. |
Similar Papers
Counterfactual-Consistency Prompting for Relative Temporal Understanding in Large Language Models (2025.acl-short)
Copied to clipboard
| Challenge: | Existing work has highlighted that large language models lack temporal reasoning abilities, especially when attempting to infer temporal relationships without relying on absolute time indicators. |
| Approach: | They propose a method that generates counterfactual questions and enforces collective constraints, enhancing the model’s consistency. |
| Outcome: | The proposed method shows significant improvements in event ordering for explicit and implicit events and temporal commonsense understanding. |
Improving In-Context Learning with Prediction Feedback for Sentiment Analysis (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning paradigm. |
| Approach: | They propose a framework that incorporates prior predictions and feedback to improve sentiment understanding by incorporating prior feedback and leveraging a feedback-driven prompt. |
| Outcome: | The proposed framework improves on nine sentiment analysis datasets with an average improvement of 5.95% over conventional methods. |
FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing faithful RAG approaches enforce strict context adherence, but they forcibly suppress the model’s parametric knowledge, which undermines the model's internal knowledge structure and increases the risk of misinterpreting the context. |
| Approach: | They propose a framework that resolves knowledge conflicts by explicitly modeling discrepancies between the model’s parametric knowledge and retrieved context. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in knowledge conflict cases and identifies conflicting knowledge at the fact level and designs a self-thinking process. |
Prompting Large Language Models for Counterfactual Generation: An Empirical Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks, but their ability to generate counterfactuals has not been examined systematically. |
| Approach: | They propose a framework to evaluate LLMs' ability to generate counterfactuals based on key factors including intrinsic properties and prompt design. |
| Outcome: | The proposed framework examines the strengths and weaknesses of large language models (LLMs) and identifies factors that influence their ability to generate counterfactuals. |
Mapping the Course for Prompt-based Structured Prediction (2026.eacl-long)
Copied to clipboard
| Challenge: | Large language models have demonstrated strong performance in a wide-range of language tasks without task-specific fine-tuning. |
| Approach: | They combine large language models with combinatorial inference to marry predictive power of LLMs with structural consistency provided by inference methods. |
| Outcome: | The proposed model incorporates symbolic inference to provide consistent and accurate predictions on challenging tasks. |
You don’t need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments (2024.naacl-long)
Copied to clipboard
Bangzhao Shu, Lechen Zhang, Minje Choi, Lavinia Dunagan, Lajanugen Logeswaran, Moontae Lee, Dallas Card, David Jurgens
| Challenge: | Large Language Models (LLMs) are popular for research in social sciences . currently, prompting LLMs is insufficient to accurately and reliably capture model perceptions, and we discuss potential alternatives to improve this. |
| Approach: | They construct a dataset that contains 693 questions encompassing 39 different instruments of persona measurement on 115 persona axes and a set of questions containing minor variations. |
| Outcome: | The proposed model can generate answers and negate statements in a consistent and robust manner. |
Faithful Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance (2026.acl-long)
Copied to clipboard
| Challenge: | Prior work has focused on generating convincing rationales that appear to be subjectively faithful, but it remains unclear whether these explanations are epistemic faithful. |
| Approach: | They propose a method that enhances epistemic faithfulness by guiding explanation generation through attention-level interventions, informed by token-level heatmaps. |
| Outcome: | The proposed method significantly improves epistemic faithfulness across multiple models, benchmarks, and prompts. |
Context-DPO: Aligning Language Models for Context-Faithfulness (2025.findings-acl)
Copied to clipboard
Baolong Bi, Shaohan Huang, Yiwei Wang, Tianchi Yang, Zihan Zhang, Haizhen Huang, Lingrui Mei, Junfeng Fang, Zehao Li, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang, Shenghua Liu
| Challenge: | Context-DPO is the first alignment method specifically designed to enhance contextfaithfulness for large language models. |
| Approach: | They propose a benchmark that simulates Retrieval-Augmented Generation scenarios with knowledge conflicts to evaluate context-faithfulness. |
| Outcome: | The proposed method improves LLMs' context-faithfulness by 35% to 280% over open-source models. |
Robust and Scalable Model Editing for Large Language Models (2024.lrec-main)
Copied to clipboard
Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun
| Challenge: | Existing methods that ignore contextual knowledge fail to reliably fall back to parametric knowledge when presented with irrelevant context. |
| Approach: | They propose to use contextual knowledge to update and correct LLMs' knowledge by in-context editing instead of retraining. |
| Outcome: | The proposed method outperforms current state-of-the-art methods by a large margin on a dataset that contains irrelevant questions. |
PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are useful for low-resource scenarios and time-restricted applications. |
| Approach: | They propose a large-scale evaluation tool for large language models that uses prompts . they evaluate 720 prompt templates for open-source LLM-based metrics on MT and summarization datasets a 6.6M evaluations. |
| Outcome: | The proposed model evaluates 720 prompt templates on machine translation and summarization datasets. |