In the realm of conversational AI, ambiguity in user queries can pose a significant challenge. The ClariQ challenge, part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020, highlights this issue and aims to develop systems that can handle such situations effectively. Instead of providing a direct answer to an ambiguous question, these systems should return relevant clarifying questions. This article walks you through the process of implementing such a system using a few lines of Python code with Hugging Face’s NLP models.

Understanding the Problem

Imagine you’re at a restaurant, and you tell the waiter, “I’d like some pasta.” The waiter might ask, “Are you looking for something specific, like vegetarian or with seafood?” This back-and-forth is essential for improving understanding and providing the best dining experience. Likewise, in a conversational AI setting, when a user poses an ambiguous question, the system should not guess but instead seek clarification before responding.

Setting Up Your Environment

To get started, you’ll need the Hugging Face Transformers library and the Sentence Transformer’s library. Make sure to have the following packages installed:

• transformers
• sentence-transformers
• torch

Generating Clarifications

We will be using the model AshishkrDialog_clarification_gpt2 to generate clarifying questions. Here’s a simplified analogy to understand the process: You can think of the model as a helpful librarian eager to provide a selection of books (in this case, questions) that can best assist readers (users) in finding their desired information.

Here’s how the code looks:

from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained('AshishkrDialog_clarification_gpt2')
model = AutoModelWithLMHead.from_pretrained('AshishkrDialog_clarification_gpt2')

input_query = "Serve your models directly from Hugging Face infrastructure and run large scale NLP models in milliseconds with just a few lines of code"
query = input_query + "~~"
input_ids = tokenizer.encode(query.lower(), return_tensors='pt')

sample_outputs = model.generate(input_ids,
                                do_sample=True,
                                num_beams=1,
                                max_length=128,
                                temperature=0.9,
                                top_k=40,
                                num_return_sequences=10)

clarifications_gen = []
for i in range(len(sample_outputs)):
    r = tokenizer.decode(sample_outputs[i], skip_special_tokens=True).split()[0]
    r = r.split('~~')[1]
    if r not in clarifications_gen:
        clarifications_gen.append(r)

print(clarifications_gen)
# to select the top n results:
from sentence_transformers import SentenceTransformer, util
import torch
embedder = SentenceTransformer('paraphrase-distilroberta-base-v1')
corpus = clarifications_gen
corpus_embeddings = embedder.encode(corpus, convert_to_tensor=True)

query = input_query.lower()
query_embedding = embedder.encode(query, convert_to_tensor=True)
cos_scores = util.pytorch_cos_sim(query_embedding, corpus_embeddings)[0]
top_results = torch.topk(cos_scores, k=5)

print("Top clarifications generated:")
for score, idx in zip(top_results[0], top_results[1]):
    print(f"{corpus[idx]} (Score: {score:.4f})")

How the Code Works

The provided code performs a series of steps to refine user queries through clarification:

1. Input Preparation: The user’s input is encoded for processing.
2. Sample Output Generation: The model generates potential clarifications for the ambiguous input.
3. Embedding and Scoring: The clarifying questions are then embedded, enabling the model to score and rank them based on their relevance to the initial query.

Troubleshooting

If you encounter issues while running the code, consider the following troubleshooting steps:

• Ensure all required libraries are installed and up-to-date.
• Verify that the model names are correct and available on the Hugging Face hub.
• Check the system’s memory and processing capability, especially for large models.
• For any specific errors, consult the documentation or community forums for guidance.

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Conclusion

Generating clarifying questions from ambiguous queries is a critical skill in facilitating effective communication in AI systems. By implementing the steps outlined in this article, you can enhance your conversational AI’s capabilities, much like a librarian that helps patrons find the right book through insightful questions.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.