Papers with keyword matching
Agentic AI for Human Resources: LLM-Driven Candidate Assessment (2026.eacl-demo)
Copied to clipboard
Kamer Ali Yuksel, Abdul Basit Anees, Ashraf Hatim Elneima, Sanjika Hewavitharana, Mohamed Al-Badrashiny, Hassan Sawaf
| Challenge: | Current systems rely on keyword matching and shallow keyword-based screening, leading to missed opportunities and inconsistent evaluations. |
| Approach: | They propose a framework that uses Large Language Models to automate candidate assessment in recruitment. |
| Outcome: | The proposed framework outputs detailed assessment reports, candidate comparisons, and ranked recommendations that are transparent, auditable, and suitable for real-world hiring workflows. |
Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects (2026.acl-demo)
Copied to clipboard
Christopher Latimer, Nicolò Boschi, Andrew Neeser, Chris Bartholomew, Gaurav Srivastava, Xuan Wang, Naren Ramakrishnan
| Challenge: | Hindsight organizes long-term memory into four logical networks and exposes three core operations. |
| Approach: | Hindsight organizes long-term memory into four logical networks and exposes three core operations. |
| Outcome: | Hindsight is a working memory system for AI agents that separates facts from beliefs . the system outperforms existing models on LongMemEval and LoCoMo with 83.6% accuracy . |
Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (2025.findings-naacl)
Copied to clipboard
| Challenge: | Ancient Chinese poetry presents unique challenges for Large Language Models due to data scarcity and limited ability of general LLMs when dealing with ACP. |
| Approach: | They propose a specialized Retrieval-Augmented Generation framework to improve LLMs' performance . they use 1.1 million ancient poems and 990K related texts to address hallucination issues . |
| Outcome: | The proposed framework improves performance of LLMs in ancient Chinese poetry domain from 49.2% to 89.0%. |
Momentum Contrastive Pre-training for Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for extractive Question Answering generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-trained models to simple keyword matching. |
| Approach: | They propose a method to align the answer probability between cloze-like and natural query-passage sample pairs. |
| Outcome: | The proposed method improves on three benchmarking QA datasets on supervised and zero-shot scenarios. |
ASPECTNEWS: Aspect-Oriented Summarization of News Documents (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for generating generic summarizations can't be used to generalize to these domains without seeing in-domain training data. |
| Approach: | They use a dataset of real-world aspect-oriented summaries to annotate articles from two different news sub-domains. |
| Outcome: | The proposed approach produces better focused summaries than existing systems without seeing in-domain training data. |
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for text classification use label names of target classes as the only supervision. |
| Approach: | They propose a method that uses keyword-based keyword matching to generate pseudo labels . they propose 'pieclass' module that iteratively trains classifiers and updates pseudo labels. |
| Outcome: | The proposed method achieves better performance than existing strong baselines on seven benchmark datasets and similar performance to fully-supervised classifiers on sentiment classification tasks. |
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing adversarial models rely on keyword matching and ignore relevant contextual relations for answer prediction. |
| Approach: | They propose to use keyword matching to attack model with two biases that rely on a perturbed answer sentence and a distracting answer sentence to misguide model. |
| Outcome: | The proposed method produces fluent and grammatical adversarial contexts while maintaining gold answers. |
REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark (2025.acl-long)
Copied to clipboard
Navve Wasserman, Roi Pony, Oshri Naparstek, Adi Raz Goldfarb, Eli Schwartz, Udi Barzelay, Leonid Karlinsky
| Challenge: | Existing benchmarks do not fully capture real-world retrieval challenges . existing benchmarks lack a complete understanding of how models perform in realistic setups . |
| Approach: | They propose an automatic benchmark to address four key properties essential for real-world retrieval: (i) multi-modal documents, (ii) enhanced difficulty, ( (iv) Realistic-RAG queries and (v) accurate labeling. |
| Outcome: | The proposed model reveals significant model weaknesses, particularly in handling table-heavy documents and robustness to query rephrasing. |