Author name cluster

Trung Bui

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7 papers

2 author rows

ICML Conference 2025 Conference Paper

NoLiMa: Long-Context Evaluation Beyond Literal Matching

Ali Modarressi
Hanieh Deilamsalehy
Franck Dernoncourt
Trung Bui
Ryan A. Rossi
Seunghyun Yoon 0002
Hinrich Schütze

Recent large language models (LLMs) support long contexts ranging from 128K to 1M tokens. A popular method for evaluating these capabilities is the needle-in-a-haystack (NIAH) test, which involves retrieving a "needle" (relevant information) from a "haystack" (long irrelevant context). Extensions of this approach include increasing distractors, fact chaining, and in-context reasoning. However, in these benchmarks, models can exploit existing literal matches between the needle and haystack to simplify the task. To address this, we introduce NoLiMa, a benchmark extending NIAH with a carefully designed needle set, where questions and needles have minimal lexical overlap, requiring models to infer latent associations to locate the needle within the haystack. We evaluate 13 popular LLMs that claim to support contexts of at least 128K tokens. While they perform well in short contexts ($<$1K), performance degrades significantly as context length increases. At 32K, for instance, 11 models drop below 50% of their strong short-length baselines. Even GPT-4o, one of the top-performing exceptions, experiences a reduction from an almost-perfect baseline of 99. 3% to 69. 7%. Our analysis suggests these declines stem from the increased difficulty the attention mechanism faces in longer contexts when literal matches are absent, making it harder to retrieve relevant information. Even models enhanced with reasoning capabilities or CoT prompting struggle to maintain performance in long contexts. We publicly release the dataset and evaluation code at https: //github. com/adobe-research/NoLiMa.

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NeurIPS Conference 2025 Conference Paper

Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization

Subhojyoti Mukherjee
Viet Lai
Raghavendra Addanki
Ryan Rossi
Seunghyun Yoon
Trung Bui
Anup B. Rao
Jayakumar Subramanian

Offline reinforcement learning (RL) is a variant of RL where the policy is learned from a previously collected dataset of trajectories and rewards. In our work, we propose a practical approach to offline RL with large language models (LLMs). We recast the problem as reward-weighted fine-tuning, which can be solved using similar techniques to supervised fine-tuning (SFT). To showcase the value of our approach, we apply it to learning short-horizon question-answering policies of a fixed length, where the agent reasons about potential answers or asks clarifying questions. Our work stands in a stark contrast to state-of-the-art methods in this domain, based on SFT and direct preference optimization, which have additional hyper-parameters and do not directly optimize for rewards. We compare to them empirically, and report major gains in both optimized rewards and language quality.

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TMLR Journal 2025 Journal Article

Pre-trained Vision-Language Models Learn Discoverable Visual Concepts

Yuan Zang
Tian Yun
Hao Tan
Trung Bui
Chen Sun

Do vision-language models (VLMs) pre-trained to caption an image of a durian learn visual concepts such as brown (color) and spiky (texture) at the same time? We aim to answer this question as visual concepts learned “for free” would enable wide applications such as neuro-symbolic reasoning or human-interpretable object classification. We assume that the visual concepts, if captured by pre-trained VLMs, can be extracted by their vision-language interface with text-based concept prompts. We observe that recent works prompting VLMs with concepts often differ in their strategies to define and evaluate the visual concepts, leading to conflicting conclusions. We propose a new concept definition strategy based on two observations: First, certain concept prompts include shortcuts that recognize correct concepts for wrong reasons; Second, multimodal information (e.g. visual discriminativeness, and textual knowledge) should be leveraged when selecting the concepts. Our proposed concept discovery and learning (CDL) framework is thus designed to identify a diverse list of generic visual concepts (e.g. spiky as opposed to spiky durian), which are ranked and selected based on visual and language mutual information. We carefully design quantitative and human evaluations of the discovered concepts on nine diverse visual recognition datasets, which confirm that pre-trained VLMs do learn visual concepts that provide accurate and thorough descriptions for the recognized objects. All code and models are publicly released.

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ICML Conference 2025 Conference Paper

Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage

Saehyung Lee
Seunghyun Yoon 0002
Trung Bui
Jing Shi 0005
Sungroh Yoon

Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations. Our analysis reveals that existing hallucination detection methods struggle with detailed captions. We attribute this to the increasing reliance of MLLMs on their generated text, rather than the input image, as the sequence length grows. To address this issue, we propose a multiagent approach that leverages LLM-MLLM collaboration to correct given captions. Additionally, we introduce an evaluation framework and a benchmark dataset to facilitate the systematic analysis of detailed captions. Our experiments demonstrate that the proposed evaluation method aligns better with human judgments of factuality than existing metrics. Moreover, we show that current approaches for enhancing MLLM factuality often fail in hyper-detailed image captioning tasks. In contrast, our approach significantly enhances the factual accuracy of captions, even improving those generated by GPT-4V. Finally, we highlight a limitation of VQA-centric benchmarking by demonstrating that an MLLM’s performance on VQA benchmarks may not correlate with its ability to generate detailed image captions.

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ICLR Conference 2024 Conference Paper

LRM: Large Reconstruction Model for Single Image to 3D

Yicong Hong
Kai Zhang 0045
Jiuxiang Gu
Sai Bi
Yang Zhou 0019
Difan Liu
Feng Liu 0015
Kalyan Sunkavalli

We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category-specific fashion, LRM adopts a highly scalable transformer-based architecture with 500 million learnable parameters to directly predict a neural radiance field (NeRF) from the input image. We train our model in an end-to-end manner on massive multi-view data containing around 1 million objects, including both synthetic renderings from Objaverse and real captures from MVImgNet. This combination of a high-capacity model and large-scale training data empowers our model to be highly generalizable and produce high-quality 3D reconstructions from various testing inputs, including real-world in-the-wild captures and images created by generative models. Video demos and interactable 3D meshes can be found on our LRM project webpage: https://yiconghong.me/LRM.

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AAAI Conference 2022 Conference Paper

CAISE: Conversational Agent for Image Search and Editing

Hyounghun Kim
Doo Soon Kim
Seunghyun Yoon
Franck Dernoncourt
Trung Bui
Mohit Bansal

Demand for image editing has been increasing as users’ desire for expression is also increasing. However, for most users, image editing tools are not easy to use since the tools require certain expertise in photo effects and have complex interfaces. Hence, users might need someone to help edit their images, but having a personal dedicated human assistant for every user is impossible to scale. For that reason, an automated assistant system for image editing is desirable. Additionally, users want more image sources for diverse image editing works, and integrating an image search functionality into the editing tool is a potential remedy for this demand. Thus, we propose a dataset of an automated Conversational Agent for Image Search and Editing (CAISE). To our knowledge, this is the first dataset that provides conversational image search and editing annotations, where the agent holds a grounded conversation with users and helps them to search and edit images according to their requests. To build such a system, we first collect image search and editing conversations between pairs of annotators. The assistant-annotators are equipped with a customized image search and editing tool to address the requests from the user-annotators. The functions that the assistant-annotators conduct with the tool are recorded as executable commands, allowing the trained system to be useful for real-world application execution. We also introduce a generator-extractor baseline model for this task, which can adaptively select the source of the next token (i. e. , from the vocabulary or from textual/visual contexts) for the executable command. This serves as a strong starting point while still leaving a large human-machine performance gap for useful future work. Data and code are available: https: //github. com/hyounghk/CAISE.

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IJCAI Conference 2016 Conference Paper

Practical Linear Models for Large-Scale One-Class Collaborative Filtering

Suvash Sedhain
Hung Bui
Jaya Kawale
Nikos Vlassis
Branislav Kveton
Aditya Krishna Menon
Trung Bui
Scott Sanner

Collaborative filtering has emerged as the de facto approach to personalized recommendation problems. However, a scenario that has proven difficult in practice is the one-class collaborative filtering case (OC-CF), where one has examples of items that a user prefers, but no examples of items they do not prefer. In such cases, it is desirable to have recommendation algorithms that are personalized, learning-based, and highly scalable. Existing linear recommenders for OC-CF achieve good performance in benchmarking tasks, but they involve solving a large number of a regression subproblems, limiting their applicability to large-scale problems. We show that it is possible to scale up linear recommenders to big data by learning an OC-CF model in a randomized low-dimensional embedding of the user-item interaction matrix. Our algorithm, Linear-FLow, achieves state-of-the-art performance in a comprehensive set of experiments on standard benchmarks as well as real data.

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