Do LLMs Really Adapt to Domains? An Ontology Learning Perspective (2407.19998v1)

Abstract: LLMs have demonstrated unprecedented prowess across various natural language processing tasks in various application domains. Recent studies show that LLMs can be leveraged to perform lexical semantic tasks, such as Knowledge Base Completion (KBC) or Ontology Learning (OL). However, it has not effectively been verified whether their success is due to their ability to reason over unstructured or semi-structured data, or their effective learning of linguistic patterns and senses alone. This unresolved question is particularly crucial when dealing with domain-specific data, where the lexical senses and their meaning can completely differ from what a LLM has learned during its training stage. This paper investigates the following question: Do LLMs really adapt to domains and remain consistent in the extraction of structured knowledge, or do they only learn lexical senses instead of reasoning? To answer this question and, we devise a controlled experiment setup that uses WordNet to synthesize parallel corpora, with English and gibberish terms. We examine the differences in the outputs of LLMs for each corpus in two OL tasks: relation extraction and taxonomy discovery. Empirical results show that, while adapting to the gibberish corpora, off-the-shelf LLMs do not consistently reason over semantic relationships between concepts, and instead leverage senses and their frame. However, fine-tuning improves the performance of LLMs on lexical semantic tasks even when the domain-specific terms are arbitrary and unseen during pre-training, hinting at the applicability of pre-trained LLMs for OL.

Overview of Domain Adaptation in LLMs: An Ontology Learning Perspective

This paper addresses an essential question in the paper of LLMs: their ability to adapt to domain-specific data for ontology learning tasks. Researchers Huu Tan Mai, Cuong Xuan Chu, and Heiko Paulheim critically examine whether LLMs inherently adapt to novel domains or if their successful performance hinges primarily on the lexical senses acquired during pre-training. They scrutinize this through carefully controlled experiments involving two main ontology learning tasks: relation extraction and taxonomy discovery.

Experimental Procedure

The researchers utilize WordNet to create a set of domain-specific corpora with parallel representations in gibberish, simulating environments outside the LLMs' prior training. This method achieves a controlled and rigorous evaluation framework by stripping away recognizable lexical senses to focus solely on relational and semantic reasoning. The synthesized domains—Sweets, Football, and Music—are explored with varying hypernym relationships, and tests are conducted across widely used LLMs including GPT-3.5, GPT-4, Falcon-40B, LLaMa2-13B, and Zephyr-7B-$\beta$.

Key Findings

The paper reveals a significant decline in LLM performance when domain concepts are replaced with gibberish equivalents, yet some ability to perform ontology learning tasks remains. This indicates a strong dependency on lexical priors rather than a genuine reasoning capability to extrapolate principles of ontology learning from domain-specific relationships. The paper shows that the semantic priors LLMs develop during pre-training on vast corpora entail limitations in generalizing to new, arbitrary domains where semantics are unknown or altered.

A salient result is that fine-tuning facilitates adaptation to domain-specific taxonomy discovery tasks, even when using gibberish datasets. Notably, fine-tuned models demonstrated improved performance in relation discovery tasks beyond their pre-tuning baseline, although never fully achieving equivalency with their performance on real-word datasets. The paper adeptly highlights that fine-tuning can enhance a model’s sensitivity to syntactic and relational clues beyond its pre-trained lexical knowledge.

Implications and Future Directions

The implications of these findings have substantial theoretical and practical relevance. Theoretically, the research emphasizes the difficulty LLMs face in abstract reasoning within unfamiliar ontologies absent lexical anchors. Practically, this underlines the necessity of specific adaptation and fine-tuning processes for LLMs to be useful in applications where domain-specific language dominates, such as specialized industry jargon or novel scientific fields.

Future avenues of research might focus on enhancing LLM architectures or training regimes that bridge current gaps in domain accommodation. Adaptation methods or hybrid models that integrate symbolic reasoning might address some of the expressed limitations, paving the way toward more versatile LLM applications.

Conclusion

In conclusion, this paper makes critical strides in understanding LLM adaptability by dissecting their performance through the lens of ontology learning. It underscores the inherent challenges LLMs face when stripped of learned lexical context, critically revealing the need for ongoing adaptation, particularly through fine-tuning. As interest in deploying LLMs across diverse and nuanced fields expands, these insights offer significant value for both the research community and industry practitioners looking to maximize the utility of AI in complex, domain-specific contexts.