Papers by Jinheon Baek
Efficient Long Context Language Model Retrieval with Compression (2025.acl-long)
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| Challenge: | Long Context Language Models (LCLMs) are a new paradigm for Information Retrieval . however, processing large number of passages within in-context for retrieval is computationally expensive . a proposed compression approach for LCLM retrieval maximizes retrieval performance while minimizing the length of the compressed passages. |
| Approach: | They propose a new compression approach tailored to maximize retrieval performance while minimizing the length of compressed passages. |
| Outcome: | The proposed approach maximizes retrieval performance while minimizing the length of compressed passages while reducing the in-context size by 1.91. |
Unified Multimodal Interleaved Document Representation for Retrieval (2026.findings-eacl)
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| Challenge: | Existing methods focus on textual content, ignoring the fact that documents can contain multiple modalities. |
| Approach: | They propose a method that holistically embeds documents interleaved with multiple modalities . they use vision-language models that combine text, images, and tables into a unified format . |
| Outcome: | The proposed method outperforms baselines on textual and multimodal queries. |
Efficient Real-time Refinement of Language Model Text Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) generate factually incorrect answers, a challenge that remains . Streaming-VR enables on-the-fly verification and correction of tokens as they are generated . |
| Approach: | They propose a method that enables on-the-fly verification and correction of LLM tokens as they are generated. |
| Outcome: | The proposed method improves factual accuracy and improves refinement efficiency compared to prior methods. |
Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments (2025.findings-acl)
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Patomporn Payoungkhamdee, Pume Tuchinda, Jinheon Baek, Samuel Cahyawijaya, Can Udomcharoenchaikit, Potsawee Manakul, Peerat Limkonchotiwat, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Multi-step reasoning is essential for large language models, yet multilingual performance remains challenging. |
| Approach: | They propose a framework to evaluate Program-of-Thought (PoT) prompting by separating multilingual reasoning from code execution to examine impact of fine-tuning on question-reasoning alignment and reasoning quality. |
| Outcome: | The proposed framework outperforms CoT fine-tuned models in multilingual settings and shows strong correlation between reasoning quality and answer accuracy. |
Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have enabled strong reasoning capabilities through Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a framework that integrates cognitively inspired reasoning paradigms with linguistic constraints to reduce token usage while preserving reasoning accuracy. |
| Outcome: | The proposed framework reduces token usage while preserving reasoning accuracy across 18 reasoning datasets across multiple domains, languages, and modalities. |
UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. |
| Approach: | They propose a framework to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. |
| Outcome: | The proposed framework shows superiority over existing methods on 10 benchmarks of multiple modalities. |
VideoRAG: Retrieval-Augmented Generation over Video Corpus (2025.findings-acl)
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| Challenge: | Existing approaches to generating models rely on text and images, but video content is a rich source of multimodal knowledge. |
| Approach: | They propose a framework that dynamically retrieves videos based on their relevance with queries . they use large video language models to represent video content for retrieval . |
| Outcome: | The proposed framework retrieves videos based on relevance with queries and integrates both visual and textual information. |
KALA: Knowledge-Augmented Language Model Adaptation (2022.naacl-main)
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| Challenge: | Pre-trained language models (PLMs) have proved to be effective on various natural language understanding tasks. |
| Approach: | They propose a domain adaption framework which modulates the intermediate hidden representations of PLMs with domain knowledge, consisting of entities and their relational facts. |
| Outcome: | The proposed framework outperforms adaptive pre-training on question answering and named entity recognition tasks on multiple datasets across different domains. |
Database-Augmented Query Representation for Information Retrieval (2025.emnlp-main)
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| Challenge: | Information retrieval models that aim to search for documents relevant to a query have shown multiple successes, but the query from the user is oftentimes short, which challenges the retrievers to correctly fetch relevant documents. |
| Approach: | They propose a database-augmented Query representation framework which augments the query with various (query-related) metadata across multiple tables. |
| Outcome: | The proposed framework significantly improves overall retrieval performance over baselines. |
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models (2025.naacl-long)
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| Challenge: | a new system that leverages the encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models (LLMs) is proposed to enhance the productivity of researchers . a researcher's research idea generation process involves problem identification, method development, experiment design and iterative revision . |
| Approach: | They propose a system that leverages encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models to assist researchers in their work. |
| Outcome: | The proposed system generates novel ideas based on human and model-based evaluations . it leverages encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models based systems . |
An Empirical Study of Multilingual Reasoning Distillation for Question Answering (2024.emnlp-main)
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Patomporn Payoungkhamdee, Peerat Limkonchotiwat, Jinheon Baek, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Existing efforts to distill reasoning capabilities have focused mainly on English, leaving multilingual distillation underexplored. |
| Approach: | They propose a method that incorporates incorrect rationales as additional guidance to improve multilingual reasoning in large language models. |
| Outcome: | Empirical results show that d-CoT-nR significantly surpasses the baseline, improving accuracy in unseen languages and correctness in step-by-step reasoning. |
Rethinking Code Refinement: Learning to Judge Code Efficiency (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown impressive capabilities in understanding and generating codes. |
| Approach: | They propose a method that is trained to judge the efficiency between two different versions of code by either classifying the superior one or predicting the relative improvement. |
| Outcome: | The proposed method can distinguish between more and less efficient versions of code on multiple programming languages with multiple refinement steps. |
Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement (2023.eacl-main)
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| Challenge: | Existing work uses predicted answers instead of unavailable ground-truth answers as conversation history for inference. |
| Approach: | They propose to filter out inaccurate answers in the conversation history without making any architectural changes to the model. |
| Outcome: | The proposed models outperform baselines on two standard ConvQA datasets. |
Direct Fact Retrieval from Knowledge Graphs without Entity Linking (2023.acl-long)
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| Challenge: | Existing methods to retrieve facts from Knowledge Graphs (KGs) require additional labels and may accumulate errors . |
| Approach: | They propose a framework that directly retrieves facts from KGs given input text based on their representational similarities. |
| Outcome: | The proposed framework outperforms baselines on multiple fact retrieval tasks. |
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
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| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation (2025.findings-emnlp)
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Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan, Kareem Elzeky, Henok Biadglign Ademtew, Alham Fikri Aji, Vladimir Araujo, Israel Abebe Azime, Jinheon Baek, Frederico Belcavello, Fermin Cristobal, Jan Christian Blaise Cruz, Mary Dabre, Raj Dabre, Toqeer Ehsan, Naome A Etori, Fauzan Farooqui, Jiahui Geng, Guido Ivetta, Thanmay Jayakumar, Soyeong Jeong, Zheng Wei Lim, Aishik Mandal, Sofía Martinelli, Mihail Minkov Mihaylov, Daniil Orel, Aniket Pramanick, Sukannya Purkayastha, Israfel Salazar, Haiyue Song, Tiago Timponi Torrent, Debela Desalegn Yadeta, Injy Hamed, Atnafu Lambebo Tonja, Thamar Solorio
| Challenge: | a human-curated benchmark of over 5,800 triples of images is used to evaluate multimodal translation systems. |
| Approach: | They introduce a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. |
| Outcome: | The results show that visual context improves translation quality in culturally-specific items . |
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation (2022.acl-short)
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| Challenge: | Existing sparse retrieval models rely on term-based matching to retrieve relevant documents. |
| Approach: | They propose a framework which augments the representations of documents with interpolation and perturbation. |
| Outcome: | The proposed framework significantly outperforms baselines on the dense retrieval of both the labeled and unlabeled documents. |
Phrase Retrieval for Open Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning (2023.findings-acl)
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| Challenge: | Open-Domain Conversational Question Answering (ODConvQA) aims to answer questions through a multi-turn conversation . however, such a pipeline approach makes the reader vulnerable to errors propagated from the retriever, which makes it slower since they are not runnable in parallel. |
| Approach: | They propose a method to directly predict answers with a phrase retrieval scheme for a sequence of words. |
| Outcome: | The proposed method outperforms the baselines on two ODConvQA datasets. |
Knowledge-Augmented Language Model Verification (2023.emnlp-main)
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| Challenge: | Recent Language Models (LMs) generate factually incorrect answers to queries . authors propose to augment LMs with knowledge retrieved from external source . |
| Approach: | They propose to augment LMs with knowledge retrieved from external sources . they validate the output and knowledge of the knowledge-augmented LM with a separate verifier . |
| Outcome: | The proposed model can generate factually incorrect answers on multiple questions . a verifier detects retrieval errors and can correct them by retrieving new knowledge or generating new text . |
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)
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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment. |
| Approach: | They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation . |
| Outcome: | The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks. |
Revisiting In-Context Learning with Long Context Language Models (2025.findings-acl)
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| Challenge: | In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. |
| Approach: | They revisited previous studies using in-context learning techniques . they found that using a data augmentation approach, they significantly improved ICL performance . |
| Outcome: | The proposed approach significantly improves ICL performance on 18 datasets spanning 4 tasks . the proposed approach does not improve performance over a simple random sample selection method . |
Test-Time Self-Adaptive Small Language Models for Question Answering (2023.findings-emnlp)
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| Challenge: | Recent instruction-finetuned large language models (LMs) have shown notable performances in various tasks, such as question-answering. |
| Approach: | They propose to use unlabeled test data to transfer smaller language models with limited knowledge. |
| Outcome: | The proposed strategy shows significant performance improvements on benchmark QA datasets with higher robustness across diverse prompts, enabling LMs to stay stable. |
Knowledge Base Construction for Knowledge-Augmented Text-to-SQL (2025.findings-acl)
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Jinheon Baek, Horst Samulowitz, Oktie Hassanzadeh, Dharmashankar Subramanian, Sola Shirai, Alfio Gliozzo, Debarun Bhattacharjya
| Challenge: | Existing approaches to translate natural language queries into SQL statements are limited in their parametric knowledge of the database schemas. |
| Approach: | They propose to construct a knowledge base for text-to-SQL, a foundational source of knowledge, from which we retrieve and generate the necessary knowledge for given queries. |
| Outcome: | The proposed approach outperforms baselines on multiple text-to-SQL datasets and shows that it is practical and reliable. |