Papers with PIC
Pragmatic Inference Chain (PIC) Improving LLMs’ Reasoning of Authentic Implicit Toxic Language (2025.emnlp-main)
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| Challenge: | Existing studies have shown that LLMs can detect toxicity by using a variety of inference-intensive tasks, such as understanding humour and metaphors. |
| Approach: | They propose a new method to prompt LLMs to identify toxic language using a set of online data that are verified by human annotators. |
| Outcome: | The proposed method significantly improves the success rate of GPT-4o, Llama-3.1-70B-Instruct, DeepSeek-v2.5, and DeepSeq-v3 in identifying implicit toxic language compared to five baseline prompts, such as CoT and rule-based baselines. |
PIC: Unlocking Long-Form Text Generation Capabilities of Large Language Models via Position ID Compression (2025.acl-long)
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| Challenge: | Long-context understanding is crucial for large language models (LLMs) however, the ability to “output-long” is underexplored. |
| Approach: | They propose a position ID compression approach to unlock the long-form text generation potential of large language models (LLMs). |
| Outcome: | The proposed approach can extend LLMs' generation length by 1.5 times without compromising generation quality. |
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game (2020.findings-emnlp)
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| Challenge: | Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. |
| Approach: | They propose to use a communication game between a speaker and a listener to generate captions for PIC. |
| Outcome: | The proposed model achieves state-of-the-art performance for personal image captioning (PIC) the proposed model is based on a communication game between a speaker and a listener . |
Document Segmentation Matters for Retrieval-Augmented Generation (2025.findings-acl)
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Zhitong Wang, Cheng Gao, Chaojun Xiao, Yufei Huang, Shuzheng Si, Kangyang Luo, Yuzhuo Bai, Wenhao Li, Tangjian Duan, Chuancheng Lv, Guoshan Lu, Gang Chen, Fanchao Qi, Maosong Sun
| Challenge: | Existing rule-based chunking methods lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence. |
| Approach: | They propose a method that leverages document summaries as pseudo-instructions to guide chunking by computing semantic similarity between sentences and the summary. |
| Outcome: | Experiments on multiple open-domain question-answering benchmarks show that PIC significantly improves retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training. |
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining (2026.findings-acl)
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Phung Gia Huy, Hai An Vu, Minh-Phuc Truong, Thang Duc Tran, Linh Ngo Van, Thanh Hong Nguyen, Trung Le
| Challenge: | Existing approaches to train dense representations require explicit coordination of how information is arranged across embedding dimensionality and model depth. |
| Approach: | They propose a framework that trains Matryoshka representations using self-distilled intra-relational alignment and Progressive information chaining. |
| Outcome: | The proposed framework produces coherent and compact Matryoshka representations with significant performance advantages under low-dimensional models. |