Papers by Emre Barut
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought (2024.lrec-main)
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| Challenge: | Existing frameworks for guiding a language model in reasoning tasks are limited by their tendency to generate low-quality rationales that are repetitive and vacuous. |
| Approach: | They propose a framework that leverages a lightweight language model for guiding a black-box large LM in reasoning tasks. |
| Outcome: | The proposed framework outperforms baselines in answer prediction accuracy. |
Prompting Vision-Language Models For Aspect-Controlled Generation of Referring Expressions (2024.findings-naacl)
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Danfeng Guo, Sanchit Agarwal, Arpit Gupta, Jiun-Yu Kao, Emre Barut, Tagyoung Chung, Jing Huang, Mohit Bansal
| Challenge: | Referring Expression Generation (REG) is the task of generating a descriptive caption that uniquely identifies a given target in the scene. |
| Approach: | They propose an Aspect-Controlled REG task which requires generating a referring expression conditioned on the input aspect(s) by changing the input input such as color, location, action etc. |
| Outcome: | The proposed model beats all prior works in the CIDEr score and achieves comparable performance to training with 100% of real data. |
Contextual Domain Classification with Temporal Representations (2021.naacl-industry)
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| Challenge: | Existing studies that incorporate context in SLU have focused on domains where context is limited to a few minutes. |
| Approach: | They propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup. |
| Outcome: | The proposed model reduces 13.04% of classification errors compared to baseline . previous studies have focused on domains where context is limited to a few minutes . |
Multimodal Context Carryover (2022.emnlp-industry)
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Prashan Wanigasekara, Nalin Gupta, Fan Yang, Emre Barut, Zeynab Raeesy, Kechen Qin, Stephen Rawls, Xinyue Liu, Chengwei Su, Spurthi Sandiri
| Challenge: | Existing voice-only dialogue systems lack multimodality support, which can lead to costly system redesigns. |
| Approach: | They propose to augment existing voice-only dialogue systems with additional multimodal components to facilitate quick delivery of visual modality support with minimal changes. |
| Outcome: | The proposed framework improves visual modality support with minimal changes on an in-house multi-modal visual navigation data set. |