Papers with token-level representations
Question Answering Infused Pre-training of General-Purpose Contextualized Representations (2022.findings-acl)
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| Challenge: | Existing pretraining objectives for question answering (QA) are not optimized for being immediately useful without fine-tuning. |
| Approach: | They propose a pre-training objective based on question answering (QA) that is based more directly on context. |
| Outcome: | The proposed model matches predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs and achieves large improvements over previous state-of-the-art models on paraphrase detection and fewshot named entity recognition. |
Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow (2024.emnlp-industry)
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| Challenge: | Quantitative investing relies on extracting quantitative features or signals from various data sources including market prices, economic indicators, financial text, etc. |
| Approach: | They propose to integrate LLMs’ token-level embeddings into a forecasting module and compare their results to those of encoder-only and decoder-based models. |
| Outcome: | The proposed model outperforms conventional sentiment scores on multiple investment universes and is based on encoder-only and decoder-based models. |
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models (2022.findings-emnlp)
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| Challenge: | End-to-end spoken language understanding systems model sequence labeling as a sequence prediction task causing a divergence from its well-established token-level tagging formulation. |
| Approach: | They propose to model sequence labeling as a sequence prediction task . their systems explicitly separate the added complexity of recognizing spoken mentions from the NLU task of sequence labelling . |
| Outcome: | The proposed systems outperform both cascaded and direct models on a labeling task of named entity recognition across SLU benchmarks. |
If Attention Serves as a Cognitive Model of Human Memory Retrieval, What is the Plausible Memory Representation? (2025.acl-long)
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| Challenge: | Recent work in computational psycholinguistics has revealed intriguing parallels between attention mechanisms and human memory retrieval, focusing primarily on vanilla Transformers that operate on token-level representations. |
| Approach: | They propose that the attention mechanism of Transformer Grammar (TG) can serve as a cognitive model of human memory retrieval using Normalized Attention Entropy (NAE) they propose that TG's attention can implement a human memory-retrieval theory known as cue-based retrieval . |
| Outcome: | The attention mechanism of Transformer Grammar (TG) achieves superior predictive power for self-paced reading times compared to vanilla Transformer’s, with further analyses revealing independent contributions from both models. |
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning (2024.emnlp-main)
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| Challenge: | Current efforts in interpretability of medical coding rely heavily on label attention mechanisms, which often leads to the highlighting of extraneous tokens irrelevant to the ICD code. |
| Approach: | They propose to leverage dictionary learning to extract sparsely activated representations from dense language models embedded in superposition to facilitate accurate interpretability. |
| Outcome: | The proposed model extracts sparsely activated representations from dense language models in superposition, even when the highlighted tokens are medically irrelevant. |
Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation (2025.emnlp-main)
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| Challenge: | Existing multimodal large language models struggle when faced with unseen domains or languages. |
| Approach: | They propose a framework that leverages the broad knowledge of an MLLM to generate cross-modal pre-questions (preQs) before retrieval. |
| Outcome: | Experiments show that PREMIR outperforms existing retrievers on out-of-distribution benchmarks, including closed-domain and multilingual settings, outperforming strong baselines across all metrics. |
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection (2026.acl-long)
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Junjun Pan, Yixin Liu, Rui Miao, Kaize Ding, Yu Zheng, Quoc Viet Hung Nguyen, Alan Wee-Chung Liew, Shirui Pan
| Challenge: | Existing graph anomaly detection methods rely on coarse sentence-level information and overlook fine-grained lexical cues, limiting their reliability and real-world applicability. |
| Approach: | They propose an explainable and fine-grained safeguarding framework for detecting malicious agents in multi-agent systems (MAS) to incorporate both coarse and fine lexical information for anomalous agent identification. |
| Outcome: | Extensive experiments across diverse MAS topologies and attack scenarios demonstrate robust detection performance and strong interpretability of XG-Guard. |