Papers with training tasks
STAND-Guard: A Small Task-Adaptive Content Moderation Model (2025.coling-industry)
Copied to clipboard
| Challenge: | Content moderation is important for developing welcoming online platforms and responsible large language models. |
| Approach: | They propose a small task-adaptive coNtent moDeration model that can be easily adapted to new or customized content moderation tasks without extensive model tuning. |
| Outcome: | The proposed model is comparable to GPT-3.5-Turbo on unseen English binary classification tasks. |
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods (2023.eacl-main)
Copied to clipboard
| Challenge: | Multi-task learning is a popular approach in natural language processing because of its commonalities and differences. |
| Approach: | They propose to summarize recent advances in multi-task learning methods based on their task relatedness into two general multi-step training methods. |
| Outcome: | The proposed methods summarize the tasks and discuss future directions. |
Direct Judgement Preference Optimization (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing judge models are largely trained with supervised finetuning on small data scales to perform limited types of evaluation tasks, limiting generalization. |
| Approach: | They propose to train judge models at large data scales with direct preference optimization . they use four training tasks to form three types of preference pairs targeting different aspects of evaluation . |
| Outcome: | The proposed model outperforms GPT-4o and other similar models on 13 benchmarks. |
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that instruction tuning is effective in instruction learning for unseen tasks, but it relies on a large amount of human-annotated samples, which restricts its generalization. |
| Approach: | They propose an instruction tuning technique which fine-tunes a pre-trained language model on a massive collection of tasks described via human-craft instructions and then tests its generalization ability on unseen tasks. |
| Outcome: | The proposed method improves IT performance versus labeled data and training tasks by constructing pseudo-labeled data from unlabele . data is used to build a model that can learn from human instructions for zero-shot generalization on unseen tasks. |
Generalization in Text-based Games via Hierarchical Reinforcement Learning (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Reinforcement Learning (RL) based agents are promising for text-based games, but their generalization remains a challenge. |
| Approach: | They propose a hierarchical framework for reinforcement learning based on knowledge graphs . they propose to decompose the game into subtasks and execute a sub-policy in the low level to conduct goal-conditioned reinforcement learning. |
| Outcome: | The proposed framework enjoys favorable generalizability on a set of difficulty levels and is able to handle complex training tasks. |
MetaICL: Learning to Learn In Context (2022.naacl-main)
Copied to clipboard
| Challenge: | Large language models can do in-context learning by conditioning on a few training examples with no parameter updates or task-specific templates. |
| Approach: | They propose a meta-training framework where a pretrained language model is tuned to do in-context learning on a large set of training tasks. |
| Outcome: | The proposed framework outperforms baseline models on 142 NLP datasets and a range of target tasks with domain shifts. |
Efficient PRM Training Data Synthesis via Formal Verification (2026.findings-acl)
Copied to clipboard
Ryo Kamoi, Yusen Zhang, Nan Zhang, Sarkar Snigdha Sarathi Das, Ranran Haoran Zhang, Wenpeng Yin, Rui Zhang
| Challenge: | Existing approaches for constructing PRM training data rely on human annotation or sampling-based labeling methods that require repeated LLM calls. |
| Approach: | They propose a framework that synthesizes PRM training data by annotating step-level error labels using formal verification tools such as Z3 and Isabelle. |
| Outcome: | The proposed framework synthesizes PRM training data from formal logic and theorem proving tasks without human annotation or additional LLM calls. |
Learning to Maximize Mutual Information for Chain-of-Thought Distillation (2024.findings-acl)
Copied to clipboard
| Challenge: | Knowledge distillation is a technique of transferring knowledge from large, complex models to smaller ones. |
| Approach: | They propose a method utilizing chain-of-thought distillation to transfer knowledge from large, complex models to smaller ones by maximizing mutual information of the representation features of the two tasks. |
| Outcome: | The proposed method outperforms the state-of-the-art knowledge distillation method on four datasets. |
Sharpness-Aware Minimization Improves Language Model Generalization (2022.acl-long)
Copied to clipboard
| Challenge: | Comparatively little work has been done to improve the generalization of language models . recent work shows that Sharpness-Aware Minimization (SAM) can improve generalization without much computational overhead. |
| Approach: | They propose a Sharpness-Aware Minimization procedure that encourages convergence to flatter minima to improve generalization of language models without much computational overhead. |
| Outcome: | The proposed Sharpness-Aware Minimization procedure can improve language models without much computational overhead. |
Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing dialog inpainting methods generate ConvQA datasets with low contextual relevance due to insufficient learning of question-answer alignment. |
| Approach: | They propose a dialog inpainting method that generates ConvQA datasets from documents . they propose re-ranking tasks and a framework that generate contextually relevant questions . |
| Outcome: | The proposed framework generates ConvQA datasets with high contextual relevance from textual sources. |
A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs. |
| Approach: | They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation . |
| Outcome: | The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets. |
Coarse-to-Fine Grounded Memory for LLM Agent Planning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to enhance LLM with offline experiences or online trajectory analysis focus on single-granularity memory derived from dynamic environmental interactions. |
| Approach: | They propose a framework that grounds coarse-to-fine memories with LLM to enable flexible adaptation to diverse scenarios. |
| Outcome: | Extensive experiments on AlfWorld, Webshop and ScienceWorld show that the proposed framework outperforms baselines and comprehensively optimizes memory-enhanced LLM Agent system. |
TaskWeb: Selecting Better Source Tasks for Multi-task NLP (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent work in NLP has shown that knowing task relationships via pairwise task transfer improves choosing one or more source tasks that help to learn a new target task. |
| Approach: | They propose a method to quantify task relationships via pairwise task transfer and build smaller training sets that improve zero-shot performances across 11 different target tasks. |
| Outcome: | The proposed method improves overall rankings and top-k precision of source tasks by 10% and 38%, respectively. |
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability. |
| Approach: | They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach. |
| Outcome: | The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios. |
Multi-Task Transfer Matters During Instruction-Tuning (2024.findings-acl)
Copied to clipboard
| Challenge: | Instruction-tuning improves a model’s ability to learn in-context, but the mechanisms that drive in-constext learning are poorly understood. |
| Approach: | They propose to train a model on hundreds of tasks to improve its ability to learn in-context. |
| Outcome: | The proposed methods improve model transfer and in-context generalization, suggesting catastrophic forgetting may impact in-constext learning. |