Papers with resource-intensive process
CASPER: Bridging Discrete and Continuous Prompt Optimization through Feedback-Guided Gradient Descent (2026.eacl-industry)
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| Challenge: | Existing pipelines for generative tasks require extensive manual effort and domain expertise to achieve task-optimal performance. |
| Approach: | They propose a framework bridging discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space. |
| Outcome: | The proposed framework bridges discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space. |
GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation (2021.emnlp-main)
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| Challenge: | Prior methods for detecting out-of-scope (OOS) utterances in text are limited and require a limited amount of data to obtain. |
| Approach: | They propose an orthogonal technique that augments existing data to train better OOS detectors operating in low-data regimes. |
| Outcome: | The proposed method outperforms existing methods on key metrics across three benchmarks and achieves relative gains of 52.4%, 48.9% and 50.3%. |
A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification (2023.acl-short)
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| Challenge: | Large language models have been able to perform well on benchmarks, but they are often not able address real-world challenges. |
| Approach: | They propose to refactor hierarchical tasks into a more indicative long-tail prediction task. |
| Outcome: | The proposed method does not require parameter updates and achieves strong performance across multiple datasets. |
Unknown Claims: Generation of Fact-Checking Training Examples from Unstructured and Structured Data (2024.emnlp-main)
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| Challenge: | Existing methods for fact-checking are labor-intensive and time-consuming. |
| Approach: | They propose a framework that generates training instances for FC systems automatically using textual and tabular content. |
| Outcome: | The proposed framework generates training instances for FC systems using textual and tabular content. |
Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization (2024.lrec-main)
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| Challenge: | Existing methods to improve language models require manual ranking and annotators. |
| Approach: | They propose a self-supervised text ranking approach for applying Proximal-Policy-Optimization to fine-tune language models while eliminating the need for human annotators. |
| Outcome: | The proposed method significantly outperforms baselines regarding BLEU, GLEU, and METEOR scores on three tasks and is consistent with humans. |
Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation (2025.findings-acl)
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Tharindu Kumarage, Ninareh Mehrabi, Anil Ramakrishna, Xinyan Zhao, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Charith Peris
| Challenge: | Safety reasoning paradigms require high-quality policy-embedded chain-of-thought datasets . generating such data through human annotations is prohibitively expensive . |
| Approach: | They propose AIDSAFE: Agentic Iterative Deliberation for Safety Reasoning . AIDS AFE leverages multi-agent deliberation to iteratively expand reasoning on safety policies . |
| Outcome: | The proposed model improves policy adherence and reasoning quality while maintaining acceptable utility and over-refusal accuracy. |