Promote, Suppress, Iterate: How Language Models Answer One-to-Many Factual Queries (2025.emnlp-main)
Copied to clipboard
| Challenge: | To answer one-to-many factual queries, a language model must simultaneously recall knowledge and avoid repeating previous answers. |
| Approach: | They propose a promote-then-suppress mechanism that enables LMs to recall all answers and suppress previously generated ones. |
| Outcome: | The proposed model first recalls all answers, and then suppresses previously generated ones. |
Similar Papers
Tracing and Dissecting How LLMs Recall Factual Knowledge for Real World Questions (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models have shown promising ability to perform commonsense reasoning. |
| Approach: | They propose a two-dimensional analysis framework that incorporates token back-tracing and token decoding to uncover how LLMs conduct factual knowledge recall. |
| Outcome: | The proposed framework shows that LLMs lack relevant knowledge but struggle to select the most accurate information based on context during the retrieval and rerank phase. |
Characterizing Mechanisms for Factual Recall in Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Language Models often integrate facts they memorized with new information that appears in a given context, causing competition within the model. |
| Approach: | They investigate distributional and mechanistic determinants of LM behavior in a dataset that queries for knowledge of world capitals . they use head attribution to identify individual attention heads that either promote the memorized answer or the in-context answer in the logits . |
| Outcome: | The proposed method can increase the rate of generating the in-context answer to 88% of the time by scaling up or down the value vector of individual attention heads at runtime. |
Challenging the Explanation Based on Preceding Tokens: Discovering Transferable Non-Literal Biasing (2026.acl-short)
Copied to clipboard
| Challenge: | et al. (2017) show that the generated preceding tokens may push the large language model towards the target answer. |
| Approach: | They find that generated preceding tokens may push large language models towards the target answer . they suggest that the LLM may intentionally use the semantically unrelated tokens to help generation of the target . |
| Outcome: | The generated preceding tokens may push the large language model towards the target answer . the biased connotations of the target response can also transfer to other prompts . |
Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions (2024.emnlp-main)
Copied to clipboard
| Challenge: | Using a method to identify next-token neurons, we find that some attention heads recognize contexts relevant to predicting a token and activate a downstream token-predicting neuron accordingly. |
| Approach: | They propose a method to identify next-token neurons and determine the upstream attention heads responsible for their activity in LLMs. |
| Outcome: | The proposed method identifies next-token neurons, finds prompts that highly activate them, and determines the upstream attention heads responsible. |
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. |
Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models have underscored their exceptional reasoning prowess with natural language understanding across a broad spectrum of tasks. |
| Approach: | They examine whether Large Language Models actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks. |
| Outcome: | The proposed model improves reasoning performance while suppressing it leads to notable degradation. |
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that pre-trained masked language models can be factual knowledge bases. |
| Approach: | They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a . |
| Outcome: | The proposed model improves on illustrative cases and external contexts . the results question the previous findings that MLMs can be reliable factual knowledge bases . |
Do All Autoregressive Transformers Remember Facts the Same Way? A Cross-Architecture Analysis of Recall Mechanisms (2025.emnlp-main)
Copied to clipboard
| Challenge: | Prior work has identified MLP modules in early layers as key contributors to factual recall. |
| Approach: | They conduct a comprehensive evaluation of factual recall across several models . they find that Qwen-based models behave differently from previous patterns . |
| Outcome: | The results show that attention modules in the earliest layers contribute more to factual recall than MLP modules. |
Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LMs) excel in retrieving popular facts, but encounter difficulty with infrequent entity-relation pairs compared to retrievers. |
| Approach: | They propose to use a WiTQA dataset to explore the effects of combinations of entities and relations on LMs. |
| Outcome: | The proposed model can retain popular relations of less common entities while retaining the same popular relations. |
Dissecting Recall of Factual Associations in Auto-Regressive Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have focused on identifying where factual knowledge is encoded in the network, but little is known about how it is extracted from the model parameters during inference. |
| Approach: | They examine how factual associations are stored and retrieved internally in LMs . they use attention edges to identify critical points where information propagates to the prediction . |
| Outcome: | The proposed model aggregates information about subject and relation to predict the correct attribute . the model “queries” the enriched subject to extract the attribute based on the proposed model . |