| Challenge: | Large language models (LLMs) have shown impressive reasoning ability, but many downstream reasoning tasks focus on performance-wise evaluation. |
| Approach: | They define and assess the Self-Contra rate across three datasets and delve into finer-grained categories of Self-contra reasoning. |
| Outcome: | The proposed model can detect self-contra reasoning with a 52.2% F1 score, much lower than for humans. |
Similar Papers
Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs (2025.emnlp-main)
Copied to clipboard
Hexiang Tan, Fei Sun, Sha Liu, Du Su, Qi Cao, Xin Chen, Jingang Wang, Xunliang Cai, Yuanzhuo Wang, Huawei Shen, Xueqi Cheng
| Challenge: | Existing detection methods fail to account for **self-consistent error** . study identifies self-consistency errors and evaluates them . |
| Approach: | They propose a method that fuses hidden state evidence from an external verifier LLM to detect self-consistent errors. |
| Outcome: | The proposed method significantly enhances performance on self-consistent errors across three LLM families. |
A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing models of large language models struggle with complex logical reasoning problems. |
| Approach: | They propose to use large language models to identify their own errors to improve their models' performance. |
| Outcome: | The proposed models can identify logical fallacies accurately and improve by themselves. |
Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to self-detection only retrospectively evaluate LLM-generated answers, leading to over-trust in incorrectly generated answers. |
| Approach: | They propose a self-detection paradigm that considers the comprehensive answer space beyond LLM-generated answers to mitigate the over-trust in LLM generated incorrect answers. |
| Outcome: | The proposed framework can be integrated with existing approaches for superior self-detection. |
ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Detecting contradictions in texts is often regarded as determining relation between hypothesis and piece of premise. |
| Approach: | They propose a human-annotated dataset to study self-contradictions in long documents . they analyze the capabilities of four open-source and commercially available LLMs . |
| Outcome: | The proposed dataset outperforms open-source LLMs on document-level tasks but struggles with self-contradictions that require more nuance and context. |
Think Wider, Detect Sharper: Reinforced Reference Coverage for Document-Level Self-Contradiction Detection (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent approaches to document-level contradiction detection (DSCD) only gain marginal improvement and often introduce inconsistencies across repeated responses. |
| Approach: | They propose a method that combines supervised fine-tuning and reinforcement learning to enhance document-level contradiction detection (DSCD) they propose to use a task-specific reward function to expand the model’s reasoning scope, boosting both accuracy and consistency. |
| Outcome: | The proposed method significantly boosts Llama 3.1-8B-Instruct’s accuracy from 38.5% to 51.1%, and consistency from 59.6% to76.2%. |
Large Language Models are Better Reasoners with Self-Verification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to solve complex natural language processing tasks require multiple steps to verify the answers. |
| Approach: | They propose to use chain of thought prompting to solve reasoning tasks with large language models. |
| Outcome: | The proposed method can improve reasoning performance on arithmetic, commonsense, and logical reasoning datasets. |
To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have remarkable performance in a variety of tasks due to factual knowledge accumulated during pre-training. |
| Approach: | They propose an evaluation protocol that disentangles knowing from applying knowledge and test state-of-the-art LLMs on 49 ambiguous entities. |
| Outcome: | The proposed evaluation protocol disentangles knowing from applying knowledge and tests state-of-the-art LLMs on 49 ambiguous entities. |
RiddleBench: A New Generative Reasoning Benchmark for LLMs (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) show remarkable capabilities, but complex reasoning skills require deeper investigation. |
| Approach: | They propose a benchmark of 1,737 puzzles to test reasoning beyond simple pattern matching. |
| Outcome: | The proposed model performs poorly when faced with reordered constraints or irrelevant information. |
Benchmarking LLM’s Capability in Reasoning over Conflicting Web References (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) integrated with retrieval-augmented generation (RAG) are a dominant framework for building intelligent assistants. |
| Approach: | They propose a benchmark to evaluate LLMs' reasoning capability over real-world conflicting documents retrieved from the web. |
| Outcome: | The proposed benchmark evaluates LLMs' reasoning capability over real-world conflicting documents retrieved from the web. |
Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that self-consistency decoding can improve performance for complex reasoning tasks with large language models. |
| Approach: | They propose a self-consistency decoding strategy that generates multiple paraphrases for each test question and then generates reasoning paths for the original and all the paraphrased questions based on greedy decoding. |
| Outcome: | The proposed strategy reduces the sampling number and improves performance on complex reasoning tasks. |