Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models can answer many questions correctly, but can also hallucinate and give wrong answers. |
| Approach: | They propose a question-answering benchmark for Wikidata that uses SPARQL to ground large language models. |
| Outcome: | The proposed method outperforms the state-of-the-art for QALD-7 by 3.6% in F1 score. |
Similar Papers
From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods focus on correcting the output but overlook the ability of LLMs to detect and correct misleading content in the input itself. |
| Approach: | They propose a three-stage fine-tuning method that improves LLMs' ability to detect and correct misleading information in input queries. |
| Outcome: | The proposed method improves accuracy and factuality of LLM responses while also reducing hallucinations. |
Few-Shot Semantic Parsing for New Predicates (2021.eacl-main)
Copied to clipboard
| Challenge: | a recent study shows that state-of-the-art neural semantic parsers are less accurate when there is only a handful of utterance-logical form pairs per predicate. |
| Approach: | They propose to use a meta-learning method to train a few-shot learning problem . they also propose to regularize attention scores with alignment statistics and apply a smoothing technique . |
| Outcome: | The proposed method outperforms baselines in one and two-shot settings. |
Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT - A Text-to-SQL Parsing Comparison (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a number of open-source large language models claim to be performing better than commercial ones . however, these models fall short of the performance achieved by closed-source models like GPT-3.5 . |
| Approach: | They evaluate six popular large language models against each other to evaluate their performance . authors say open-source models are not as effective as those built by commercial models . |
| Outcome: | a new set of models claim to match or surpass the language understanding abilities of commercial models . the results show that the models performed far below the performance of closed-source models compared to open-source ones . |
QuickLLaMA: Query-aware Inference Acceleration for Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) struggle with capturing long-distance dependencies within sequences to deeply understand semantics. |
| Approach: | They propose a system that captures relevant information within a fixed window size and provides precise answers to queries. |
| Outcome: | The proposed system can read Harry Potter within 30s and accurately answer the questions. |
On The Ingredients of an Effective Zero-shot Semantic Parser (2022.acl-long)
Copied to clipboard
| Challenge: | Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterrances to improve linguistic diversity. |
| Approach: | They propose to bridge gaps between canonical and real-world user-issued examples by using stronger paraphrasers and improved grammars. |
| Outcome: | The proposed model achieves strong performance on two semantic parsing benchmarks with zero labeled data. |
Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering (2024.eacl-long)
Copied to clipboard
| Challenge: | Recent approaches to multi-hop question answering rely on in-context learning . however, these models contain billions of parameters making them inefficient at inference time. |
| Approach: | They propose a framework that allows for improving smaller language models with less than 10 human-annotated QA pairs by synthesizing millions of multi-hop questions and claims to fine tune language models. |
| Outcome: | The proposed framework improves model performance on multi-hop question answering and fact verification benchmarks while being almost one-third the size in parameter count. |
Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study shows that large language models can perform precise text editing tasks. |
| Approach: | InstrEditBench is a benchmark dataset that compares 30,000 structured editing tasks . experimental evaluations show FineEdit outperforms state-of-the-art models . |
| Outcome: | The proposed model outperforms state-of-the-art models on single-turn edits and mistral-7B-OpenOrca on direct edits. |
Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in comprehending and analyzing lengthy sequential inputs. |
| Approach: | They propose to implement ad-hoc solutions that enhance LLMs’ performance on long input sequences by up to 50% while reducing API cost and latency by up . to address this limitation, they propose to use three datasets and two tasks to analyze news categorization and sentence analysis to evaluate their models. |
| Outcome: | The proposed solutions significantly improve LLMs’ performance on long input sequences by up to 50% while reducing API cost and latency by up . to 93% and 50%, respectively. |
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have enabled advances in the field of natural language processing . however, their application and potential are still underexplored . |
| Approach: | They evaluate four state-of-the-art instruction-tuned Large Language Models on 13 NLP tasks in English. |
| Outcome: | The evaluated models outperform state-of-the-art models on 13 real-world clinical and biomedical NLP tasks in English. |
Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)
Copied to clipboard
Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme
| Challenge: | Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing. |
| Approach: | They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English . |
| Outcome: | The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data. |