Search Results for author:
Found 11 papers, 2 papers with code
KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations
Syntactic parsers have dominated natural language understanding for decades.
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation
Our results indicate a significant performance drop in GPT-3. 5 on the unfamiliar Termite dataset, even with ATD modifications, highlighting the effect of Data Contamination on LLMs in Text-to-SQL translation tasks.
When Large Language Models contradict humans? Large Language Models' Sycophantic Behaviour
Large Language Models have been demonstrating the ability to solve complex tasks by delivering answers that are positively evaluated by humans due in part to the intensive use of human feedback that refines responses.
Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts
Reasoning methods, best exemplified by the well-known Chain-of-Thought (CoT), empower the reasoning abilities of Large Language Models (LLMs) by eliciting them to solve complex tasks in a step-by-step manner.
HANS, are you clever? Clever Hans Effect Analysis of Neural Systems
Following a correlation between first positions and model choices due to positional bias, we hypothesized the presence of structural heuristics in the decision-making process of the It-LLMs, strengthened by including significant examples in few-shot scenarios.
Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations
This disparity is demanded in further fine-tuning and affecting the cross-lingual abilities of LLMs.
A Trip Towards Fairness: Bias and De-Biasing in Large Language Models
In this paper, we performed a large investigation of the bias of three families of CtB-LLMs, and we showed that debiasing techniques are effective and usable.
PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models
Pre-trained Language Models such as BERT are impressive machines with the ability to memorize, possibly generalized learning examples.
Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages
The impressive achievements of transformers force NLP researchers to delve into how these models represent the underlying structure of natural language.
The Dark Side of the Language: Pre-trained Transformers in the DarkNet
Pre-trained Transformers are challenging human performances in many NLP tasks.
Lacking the embedding of a word? Look it up into a traditional dictionary
In this paper, we propose to use definitions retrieved in traditional dictionaries to produce word embeddings for rare words.
