Papers with zero-shot evaluation
PRover: Proof Generation for Interpretable Reasoning over Rules (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work shows that transformers can act as “soft theorem provers” by answering questions over explicitly provided knowledge in natural language. |
| Approach: | They propose a transformer-based model that answers binary questions over rule-bases and generates the corresponding proofs. |
| Outcome: | The proposed model generates proofs with an accuracy of 87% while maintaining or improving performance on the QA task. |
NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model (2025.acl-industry)
Copied to clipboard
Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko, Zhen Wan, Xuesong Yang, Zih-Ching Chen, Krishna C Puvvada, Ke Hu, Szu-Wei Fu, Jun Wei Chiu, Jagadeesh Balam, Boris Ginsburg, Yu-Chiang Frank Wang, Chao-Han Huck Yang
| Challenge: | Existing methods to train a model on a mixture of domain datasets require separate correction language models. |
| Approach: | They propose a multi-task correction MoE that trains experts to become an "expert" of speech-to-text, language-totext and vision-to text datasets by learning to route each dataset’s tokens to its mapped expert. |
| Outcome: | The proposed model outperforms GPT-3.5 and Claude-3.5-Sonnet on the Open ASR Leaderboard and reaches an average relative 5.0% WER reduction and substantial improvements in BLEU scores. |
Efficient Diagnosis Assignment Using Unstructured Clinical Notes (2023.acl-short)
Copied to clipboard
| Challenge: | Electronic phenotyping entails using electronic health records (EHRs) to identify patients with specific clinical outcomes and determine when those outcomes occurred. |
| Approach: | They propose a framework for electronic phenotyping that integrates labeling functions and a disease-agnostic neural network to assign diagnoses to patients. |
| Outcome: | The proposed framework disambiguates hypertension true positives and false positives with a supervised area under the precision-recall curve (AUPRC) of 0.85. |
oLMpics-On What Language Model Pre-training Captures (2020.tacl-1)
Copied to clipboard
| Challenge: | Recent success of pre-trained language models has spurred widespread interest in their capabilities. |
| Approach: | They propose an evaluation protocol that includes zero-shot evaluation and no fine-tuning . they propose to compare the learning curve of a fine- tuned LM to the learning of multiple controls . |
| Outcome: | The proposed evaluation protocol compares the learning curve of a fine-tuned LM to the learning of multiple controls. |
Teaching a Multilingual Large Language Model to Understand Multilingual Speech via Multi-Instructional Training (2024.findings-naacl)
Copied to clipboard
| Challenge: | Recent advances in language modeling have led to the emergence of large language models capable ofvarious natural language processing tasks. |
| Approach: | They propose a multi-instructional training approach that integrates a large language model with a speech encoder to harness the capabilities of LLMs for speech recognition and beyond. |
| Outcome: | The proposed model can be trained and aligned with a multilingual LLM on 1900 hours of transcribed data from 139 languages. |
WangchanThaiInstruct: An instruction-following Dataset for Culture-Aware, Multitask, and Multi-domain Evaluation in Thai (2025.emnlp-main)
Copied to clipboard
Peerat Limkonchotiwat, Pume Tuchinda, Lalita Lowphansirikul, Surapon Nonesung, Panuthep Tasawong, Alham Fikri Aji, Can Udomcharoenchaikit, Sarana Nutanong
| Challenge: | Existing benchmarks for large language models rely on translations, missing cultural and domain specificity. |
| Approach: | They present a human-authored dataset for evaluation and instruction tuning in Thai . findings highlight need for culturally and professionally grounded instruction data . |
| Outcome: | a human-authored dataset for evaluation and instruction tuning in Thai outperforms translation-based models . findings highlight need for culturally and professionally grounded instruction data . |
BenLLM-Eval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP (2024.lrec-main)
Copied to clipboard
Mohsinul Kabir, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, M Saiful Bari, Enamul Hoque
| Challenge: | Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing. |
| Approach: | They propose to evaluate LLMs in Bengali to benchmark their performance . they select Bangla NLP tasks such as text summarization, question answering, paraphrasing . |
| Outcome: | The proposed model performs better in some tasks than current models, but in most tasks, it is poor . |
Rethinking Negative Instances for Generative Named Entity Recognition (2024.findings-acl)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) models are constrained by a pre-defined label set and require extensive human annotations, which limits their flexibility and adaptability to unseen tasks. |
| Approach: | They propose a Generative NER system that shows improved zero-shot performance across unseen entity domains by introducing contextual information and delineating label boundaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods in zero-shot evaluation. |
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? (2021.findings-acl)
Copied to clipboard
| Challenge: | a new task is proposed to modify a model's unethical behavior by communicating context-specific principles of ethics and equity to it. |
| Approach: | They propose a task to amend a questionanswering model's unethical behavior . they use social stereotypes to augment social stereotype models with ethical interventions . |
| Outcome: | The proposed task improves model behavior but fails to generalize to new domains . it is based on a zero-shot evaluation of social stereotypes in reading comprehension systems . |
Persian Natural Language Inference: A Meta-learning Approach (2022.coling-1)
Copied to clipboard
| Challenge: | In general, shared representations are learned separately, either across tasks or across languages. |
| Approach: | They propose a meta-learning approach for inferring natural language in Persian . they use different task information or other language information to form additional high-quality tasks . |
| Outcome: | The proposed method outperforms the baseline approach, improving accuracy by roughly six percent. |
Internal and external pressures on language emergence: least effort, object constancy and frequency (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies show that the emergent languages rarely display salient features inherent to natural languages, such as compositionality of meaning and generalisation to novel objects. |
| Approach: | They propose to formalise the principle of least effort through an auxiliary objective and explore several game variants inspired by the principle 'object constancy' they find that the proposed sources of pressure result in emerging languages with less redundancy, more focus on high-level conceptual information, and better abilities of generalisation. |
| Outcome: | The proposed sources of pressure result in emerging languages with less redundancy, more focus on high-level conceptual information, and better abilities of generalisation. |
Breaking ReLU Barrier: Generalized MoEfication for Dense Pretrained Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to convert pretrained dense models to MoEs are limited to ReLU-based models with natural sparsity. |
| Approach: | They propose a G-MoEfication approach for arbitrary dense models where activation sparsity assumptions no longer hold. |
| Outcome: | The proposed method reduces the inference cost associated with dense models by sparsely activating experts. |
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream task instances as a language modeling problem. |
| Approach: | They propose to reformulate downstream tasks as a language modeling problem and add a regularization that preserves pretraining weights to the model to mitigate the destructive tendency of few-shot finetuning. |
| Outcome: | The proposed model performs better on low data regimes than the standard model on few-shot finetuning. |
GOAT: A Training Framework for Goal-Oriented Agent with Tools (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models have led to remarkable progress across a wide range of natural language processing tasks. |
| Approach: | They propose a training framework that enables fine-tuning LLM agents without human annotation. |
| Outcome: | The proposed framework enables fine-tuning LLM agents without human annotation. |
Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs (2025.acl-long)
Copied to clipboard
Sumanth Doddapaneni, Mohammed Safi Ur Rahman Khan, Dilip Venkatesh, Raj Dabre, Anoop Kunchukuttan, Mitesh M Khapra
| Challenge: | Evaluating machine-generated text remains a challenge in NLP for non-English languages . current evaluation frameworks focus on English, revealing a gap in multilingual evaluations . |
| Approach: | They propose a cross-lingual auto evaluation framework that includes evaluator LLMs and a test set specifically designed for multilingual evaluation. |
| Outcome: | The proposed model aligns more closely with human judgments than proprietary models on non-English language evaluations. |