Papers with LLM backbone
Geo-Spatially Informed Models for Geocoding Unstructured Addresses (2025.coling-industry)
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| Challenge: | e-commerce companies need to geocode their customers' addresses to reduce shipping costs and improve customer experience. |
| Approach: | They propose a method to geocode customer addresses in developing countries . they propose to incorporate geo-spatial relationships using an LLM backbone . |
| Outcome: | The proposed method outperforms the current state-of-the-art system by 20% in drift accuracy within 100 meters and the state- of-the art commercial system by 54%. |
Unsupervised Text Style Transfer for Controllable Intensity (2026.findings-eacl)
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| Challenge: | Unsupervised Text Style Transfer (UTST) aims to transfer the stylistic properties of a given text without parallel text pairs. |
| Approach: | They propose a SFT-then-PPO paradigm to fine-tune an LLM with parallel data and reward functions for distinguishing stylistic intensity in hierarchical levels. |
| Outcome: | The proposed system can transfer stylistic properties without parallel text pairs even for adjacent levels of intensity. |
Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models (2025.findings-acl)
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Qin Liu, Chao Shang, Ling Liu, Nikolaos Pappas, Jie Ma, Neha Anna John, Srikanth Doss, Lluis Marquez, Miguel Ballesteros, Yassine Benajiba
| Challenge: | LLaVA-7B demonstrated a decline in safety alignment ability on multi-modal inputs compared to its LLM backbone. |
| Approach: | They propose a method to recover alignment ability from LLM backbone while preserving functional capabilities of VLMs. |
| Outcome: | The proposed framework recovers alignment ability that is inherent in the LLM backbone with minimal impact on fluency and linguistic capabilities of pre-trained VLMs. |
What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse (2025.findings-emnlp)
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| Challenge: | Media framing is a method of shaping public perceptions of issues, but the interaction between stance and media frame remains unexplored. |
| Approach: | They propose to use a dataset of climate-change memes annotated with stance and media frames to conceptualize and computationally explore this interaction. |
| Outcome: | The proposed dataset includes 1,184 climate-change memes sourced from 47 subreddits and enables analysis of frame prominence over time and communities. |
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning (2025.naacl-long)
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Yifan Peng, Krishna C Puvvada, Zhehuai Chen, Piotr Zelasko, He Huang, Kunal Dhawan, Ke Hu, Shinji Watanabe, Jagadeesh Balam, Boris Ginsburg
| Challenge: | Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models. |
| Approach: | They propose a single-stage joint speech-text SFT approach for training SpeechLMs . their model combines text-only SFT data with three types of speech-related data . |
| Outcome: | The proposed model outperforms previous SpeechLMs on speech-based QA tasks while maintaining original speech-only capabilities. |
Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)
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Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone. |
| Approach: | They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs. |
| Outcome: | The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively. |
TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Grounding (2026.acl-long)
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| Challenge: | a critical ambiguity persists regarding what constitutes "joint ASR and diarization" a unified framework for multi-speaker ASR is proposed, but it is not yet clear what constitute "diarization." |
| Approach: | They propose a unified LLM-based framework that uses Temporal Anchor Grounding for joint multi-speaker ASR and diarization. |
| Outcome: | The proposed framework improves on AMI and AliMeeting benchmarks on speaker-content alignment . the proposed framework achieves consistent improvements in Diarization Error Rate over strong baselines . |
MotifAgent: Learning Molecular Assembly through Multi-Agent Collaboration for Chemical Language Understanding (2026.findings-acl)
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| Challenge: | Existing approaches to molecular understanding are limited to static motif recognition without understanding connection rules governing how motifs assemble into valid topological structures. |
| Approach: | They propose a multi-agent reinforcement learning framework inspired by emergent collective intelligence to solve a problem where each motif is represented by an agent sharing a common LLM backbone. |
| Outcome: | Extensive experiments show that the proposed framework surpasses specialized expert models in molecular understanding tasks. |