Papers with citation generation
ALiiCE: Evaluating Positional Fine-grained Citation Generation (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing research on citation generation is limited to sentence-level statements . positional fine-grained citations can appear anywhere within sentences . |
| Approach: | They propose a framework that allows LLMs to generate citations from sentences . they use dependency tree-based methods to parse sentence-level claims into atomic claims . |
| Outcome: | The proposed framework evaluates citation quality using three metrics including positional fine-grained citation recall, precision, and coefficient of variation of citation positions. |
Explaining Relationships Among Research Papers (2025.coling-main)
Copied to clipboard
| Challenge: | Existing literature reviews focus on summarizing individual papers without addressing the need for expository and transition sentences to explain the relationships among multiple papers. |
| Approach: | They propose a feature-based, LLM-prompting approach to generate richer citation texts . they propose to use related work sections of scientific articles as proxy for the kind of short, customized, daily feed summaries . |
| Outcome: | The proposed approach captures complex relationships among multiple papers while generating richer citation texts. |
L-CiteEval: A Suite for Evaluating Fidelity of Long-context Models (2025.acl-long)
Copied to clipboard
| Challenge: | Long-context models (LCMs) have seen remarkable advancements in recent years, facilitating tasks like long-document QA. |
| Approach: | They propose an out-of-the-box suite that can assess both generation quality and fidelity in long-context understanding tasks. |
| Outcome: | The proposed suite can assess both generation quality and fidelity in long-context understanding tasks. |
Tackling Distractor Documents in Multi-Hop QA with Reinforcement and Curriculum Learning (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing work on retrieval-augmented generation systems has shown that retrievers exhibit imperfect recall and precision, limiting downstream performance. |
| Approach: | They propose a retrieval-augmented generation model that generates answers from larger sets of retrieved contexts. |
| Outcome: | The proposed model generates answers and cites relevant information from larger sets of retrieved contexts. |
Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can help in citation generation but can also amplify existing biases, such as the Matthew effect, and introduce new ones, potentially skewing scientific knowledge dissemination. |
| Approach: | They propose to use large language models to generate scholarly references for in-text citations in papers published after GPT-4's knowledge cut-off date. |
| Outcome: | The proposed model can generate scholarly references for in-text citations, but without the aid of web browsing or a search engine, the results show a similarity between human and LLM citation patterns, but with a more pronounced high citation bias. |
Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citation (2025.findings-acl)
Copied to clipboard
| Challenge: | citation generation and retrieval-augmented generation are still lacking in large language models due to hallucinations. |
| Approach: | They propose a retrieval-augmented citation generation task that requires models to generate citations considering both external and internal knowledge while providing trustworthy references. |
| Outcome: | The proposed method achieves better performance across scenarios compared to baselines . retrieval quality, question types, and model knowledge influence trustworthiness . |
Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on citation generation has focused on unambiguous settings with single answers, failing to address the complexity of real-world scenarios. |
| Approach: | They propose a task of QA with source citation in ambiguous settings where multiple valid answers exist, where multiple sources exist. |
| Outcome: | The proposed framework generates multiple answers and cites their sources, allowing users to verify the factuality of each answer and make informed decisions. |
MedCite: Can Language Models Generate Verifiable Text for Medicine? (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing LLM-based medical question answering systems lack citation generation and evaluation capabilities, raising concerns about their adoption in practice. |
| Approach: | They propose a framework that facilitates the design and evaluation of LLM citations for medical tasks and a retrieval-citation method that generates high-quality citation. |
| Outcome: | The proposed method achieves superior citation precision and recall improvements compared to strong baseline methods and correlates well with annotation results from professional experts. |