Rethinking the Reversal Curse of LLMs: a Prescription from Human Knowledge Reversal (2024.emnlp-main)
Copied to clipboard
Zhicong Lu, Li Jin, Peiguang Li, Yu Tian, Linhao Zhang, Sirui Wang, Guangluan Xu, Changyuan Tian, Xunliang Cai
| Challenge: | Existing methods for large language models (LLMs) are limited by their aggressive sample permutation and lack a detailed understanding of the underlying reasons for the reversal curse. |
| Approach: | They propose a method which enhances bidirectional entity correlation modeling and pairwise relationship reasoning to overcome the reversal curse. |
| Outcome: | The proposed method overcomes the reversal curse by augmenting the samples with entity order-reversals and semantically preserved question-answer pairs. |
Similar Papers
An Analysis and Mitigation of the Reversal Curse (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent research observes a phenomenon in large language models called the "reversal curse" when dealing with two entities, LLMs excel in handling sequences in the form of "aRb" but when asked "who is Mary Lee Pfeiffer's son?" the LLM exhibits considerable confusion and fails to provide a as the answer . |
| Approach: | They conduct the first-ever study of how the reversal curse happens in large language models . they find that LLMs excel in handling sequences in the form of "aRb" but struggle to provide a satisfactory answer when asked "who is Mary Lee Pfeiffer's son?" |
| Outcome: | The proposed study shows that the reversal curse can stem from specific training objectives . the study also shows that a reverse query can be difficult to understand . |
DiffER: Diffusion Entity-Relation Modeling for Reversal Curse in Diffusion Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing large language models exhibit unidirectional behavior when processing bidirectional relationships . authors propose a solution to alleviate the reversal curse in Diffusion LLMs . |
| Approach: | They propose a model that addresses the "reversal curse" of bidirectional behavior in large language models . they propose 'entity-aware training' and balanced data construction to alleviate asymmetry and missing relations . |
| Outcome: | The proposed model alleviates the "reversal curse" in Diffusion LLMs . the proposed model employs whole-entity masking to mitigate entity fragmentation . |
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs (2026.acl-long)
Copied to clipboard
Yihua Zhu, Qianying Liu, Jiaxin Wang, Fei Cheng, Chaoran Liu, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira
| Challenge: | Autoregressive LLMs perform well on relational tasks that require linking entities via relational words, but it is unclear whether they learn the logical semantics of such relations or whether left-to-right order bias is involved. |
| Approach: | They propose a framework that generates text from symmetric/inverse triples and trains autoregressive models from scratch. |
| Outcome: | The proposed framework generates text from symmetric/inverse triples, trains autoregressive models from scratch, and evaluates memorization, logical inference, and in-context generalization to unseen entities. |
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have achieved impressive performance across diverse tasks, but suffer from the "reversal curse" this limitation poses a challenge to the advancement of artificial general intelligence (AGI) |
| Approach: | They propose to use training data to permute training sentences into entities and feed them into the model. |
| Outcome: | The proposed method improves the performance of large language models (LLMs) on reversed questions and improves existing models. |
Exploring Reversal Mathematical Reasoning Ability for Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have been a success in the wide range of natural language understanding and reasoning tasks. |
| Approach: | They propose a training method to improve general and reversal reasoning abilities by using a reversed dataset. |
| Outcome: | The proposed method improves general and reversal reasoning abilities and alleviates the reverse curse. |
Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles (2025.acl-long)
Copied to clipboard
Munachiso S Nwadike, Zangir Iklassov, Toluwani Aremu, Tatsuya Hiraoka, Benjamin Heinzerling, Velibor Bojkovic, Hilal AlQuabeh, Martin Takáč, Kentaro Inui
| Challenge: | Existing models that use sequential data can bypass the limitations of unidirectional causality. |
| Approach: | They propose a mechanism that enables large language models to bypass unidirectional causality . they propose 'cycle tokens' that enable recall of preceding tokens from succeeding ones . |
| Outcome: | The proposed model bypasses the limitations of unidirectional causality by enabling recall of preceding contexts. |
How to Make LLMs Forget: On Reversing In-Context Knowledge Edits (2025.naacl-long)
Copied to clipboard
| Challenge: | In-context knowledge editing (IKE) is an efficient and efficient knowledge editing method (Zheng et al., 2022b; Gangadhar and Stratos, 2024) it can be misused to manipulate responses opaquely, e.g., insert misinformation or offensive content. |
| Approach: | They propose to detect and reverse IKE-edits using only the top-10 output probabilities of the next token, even in a black-box setting. |
| Outcome: | The proposed method can be detected with high accuracy even in a black-box setting, achieving over 80% accuracy in recovering original, unedited outputs across multiple LLMs. |
Reverse Modeling in Large Language Models (2025.naacl-short)
Copied to clipboard
| Challenge: | Using pre-trained LLMs with reversed text inputs can improve their performance across multiple languages. |
| Approach: | They propose a way to determine whether LLMs can understand reversed text inputs by reversing entire paragraphs or documents at the token level. |
| Outcome: | The proposed model can be used to improve understanding across multiple languages. |
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. |
Semantic Inversion, Identical Replies: Revisiting Negation Blindness in Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Negation is a common occurrence in the real world and is essential for logical reasoning as it helps understand the opposite or absence of a statement. |
| Approach: | They propose a verification framework that includes task design and measurement methods to verify this phenomenon negation blindness on the query. |
| Outcome: | The proposed framework can be used to verify the model fails to capture semantic contradictions in negated queries despite its accurate understanding of knowledge about positive queries. |