Why Relevance Isn’t Enough for AI Search: The Importance of Answerability (Hamsterdam Research)

By Ethan Lazuk

Welcome to another edition of Hamsterdam Research! 🐹

This is where we look at recent AI research papers to learn what they’re talking about and explore their hypothetical implications for the future of search and SEO/GEO strategies.

This time, we’ll look at research called, “From Topical Relevance to Answerability: Entailment Distillation for Conversational Retrieval.”

It was submitted to arXiv on September 3, 2026, and its authors are Shuai Qin, Guojia An, Weikang Guo, Pei Ke, Jiwei Wei, Yang Yang, and Jie Zou.

In short, why should SEO/GEO professionals care about this research?

The key takeaway is simply that relevance and answerability are different things. While relevance may get a passage into consideration, it’s answerability that may determine whether it is actually useful for generating an answer.

The researchers show that highly similar passages can outrank passages that actually contain the evidence needed for an answer, creating what they call an “answerability gap.” Their CLEAR framework improves retrieval by explicitly favoring answer-supporting passages over just on-topic ones.

For SEO/GEO, the broader implication is that content may need to do more than demonstrate topical relevance. Content should also clearly contain the facts, relationships, and evidence needed to resolve the specific questions users are asking.

Let’s start by reviewing the paper’s abstract.

Here’s the abstract (with my highlights):

“Existing conversational retrievers commonly treat topical relevance as a proxy for answerability. However, a passage that closely matches the dialogue context is not necessarily the one that supports the correct answer. We identify this mismatch as a systematic answerability gap. To address this issue, we propose CLEAR, a framework that shifts conversational retrieval from topical relevance to answerability. The core of CLEAR is entailment distillation, which transfers answer-passage entailment supervision into a cross-encoder reranker so that the reranker discriminates answer-supporting passages from topical distractors at inference time, without requiring answers. CLEAR is complemented by a passage-centric abductive recall module that brings low-similarity yet answerable passages into the candidate pool by inferring answerable queries from passages with an LLM. Across TopiOCQA, QReCC, and out-of-domain TREC CAsT datasets, CLEAR consistently improves top-ranked precision over strong query-rewriting and dense-retrieval baselines, with the largest gains observed in conversations involving heavier topical noise. Moreover, applying our reranker on top of an LLM-driven query rewriter yields further gains.”

Next, let’s break down the paper’s key vocabulary terms:

• Answerability: Whether a passage contains enough evidence to support the correct answer to a user’s question. The paper argues that this is distinct from topical relevance.
• Answerability Gap: The mismatch between passages that are topically similar to a query and passages that actually support the answer. This is the paper’s central concept.
• Topical Relevance: How closely a passage matches the subject or context of a query. The authors argue that relevance alone isn’t enough to determine whether a passage is useful for answering the question.
• Semantic Trap: A case where a retriever ranks a highly similar, on-topic passage above the passage that actually contains the answer.
• Answer-Supporting Passage: A passage that contains sufficient evidence to derive the correct answer, rather than merely discussing the same topic.
• Entailment Distillation: The process of training the reranker to learn whether a passage supports an answer by transferring supervision from a Natural Language Inference model.
• Conversational Retrieval: Retrieving passages in the context of a multi-turn conversation, where the system has to account for previous questions and evolving intent.
• Conversational Query Rewriting (CQR): Rewriting a context-dependent conversational query into a standalone query before retrieval.
• Abductive Retrieval / Abductive Recall: A retrieval approach that works backward from a passage by asking, in effect, “What questions could this passage answer?” This helps surface useful passages that have low surface similarity to the original query.
• Answerability-Aware Reranking: Reordering retrieved passages based not just on contextual relevance, but also on whether they are likely to support the answer.

Awesome. Let’s take a deeper look at the research paper’s contents now.

If you want to follow along, you can grab a PDF or the HTML version on arXiv.

The full paper has 6 sections.

We’ll summarize the main ones below.

Introduction

It’s best to frame this in the authors’ own words, as it provides a lot of insight into the problem they’re trying to solve for.

“Conversational Retrieval aims to accurately retrieve relevant passages from massive corpora to answer complex user questions through multi-turn interactions,” the authors begin. “As a core component of modern conversational systems and Retrieval-Augmented Generation (RAG) technologies, its retrieval quality directly determines the accuracy and reliability of the final answers.”

“Existing solutions primarily follow two paradigms: Conversational Query Rewriting (CQR) and Conversational Dense Retrieval (CDR),” they write.

“CQR reformulates context-dependent queries into standalone versions but suffers from error propagation Mao et al. (2023a), while CDR directly encodes conversational context into dense vectors for end-to-end retrieval. CDR has become the mainstream approach, with recent methods improving through knowledge distillation from human-rewritten queries Yu et al. (2021), dual alignment strategies Lai et al. (2025b), and relaxed LLM-driven distillation objectives Lupart et al. (2025). Fundamentally, these approaches train a bi-encoder to align two long, noisy sequences—a multi-turn dialogue dominated by topic-establishing turns and an answer-bearing passage dominated by text surrounding the actual answer span—through a single similarity score. The dot-product is therefore driven by overall topical agreement, while whether the passage actually answers the current question survives only as an implicit signal that any topically aligned passage can satisfy.“

“However,” they continue, “this implicit answerability signal is not robust, and the resulting mismatch becomes more pronounced as retrievers become stronger.”

Figure 1 demonstrates the “answerability gap” — a retrieval system can rank a passage highly because it is strongly related to the topic, while overlooking a less topically similar passage that actually contains the answer:

“Narrowing the answerability gap calls for two changes to the retrieval pipeline,” explain the authors.

“First, we add a discriminator that, at inference, tells answer-supporting passages apart from topical distractors via entailment distillation: during training, a frozen NLI teacher provides passage-to-answer entailment scores, and we distill this supervision into a cross-encoder reranker that operates without access to the answer at test time. Second, a traditional reranker can only re-order what the first stage hands to it, yet a substantial share of answer-supporting passages never reach the reranker at all. We therefore add a passage-centric abductive recall channel: an LLM infers, for each passage, the queries it could plausibly answer, so that low-similarity yet answerable passages enter the candidate pool of reranker through context-to-query matching rather than context-to-passage matching alone. Together, the two components instantiate our framework, CLEAR.”

What’s my take on all of this?

This paper gives us an important content optimization insight.

In short: topical relevance ≠ answerability.

Traditional retrieval thinking often asks: “Is this document relevant to the query?”

This paper argues that conversational retrieval should instead care more about: “Does this passage actually contain evidence that supports the answer?”

That’s applicable to SEO/GEO content strategies.

Imagine someone asks: “Does Brand X offer international shipping?”

A page about Brand X’s shipping policies could be topically relevant, but if it never establishes whether international shipping is offered, it may not be answerable for that question.

The paper also essentially finds that bringing in lower-similarity but actually answer-supporting passages can improve retrieval.

That supports a really interesting SEO/GEO principle: don’t optimize content merely to be relevant to a topic. Make the content capable of resolving specific information needs.

Method

We’ll skip the related work section and jump to the Method section, which we’ll summarize by explaining Figure 2:

Figure 2 shows how CLEAR works as a two-stage retrieval system designed to close the answerability gap.

On the left, (a) Dual-Channel Recall finds candidate passages in two different ways. One path uses standard dense retrieval, matching the conversation context to passages based on similarity.

The second path works backward from the passages themselves, where an LLM generates possible questions each passage could answer, then the system matches the current conversation to those generated questions. This path helps recover passages that may not look very similar to the wording of the query but are still capable of answering it.

The two candidate lists are then combined and passed to the second stage.

On the right, (b) Entailment-Aware Reranking decides which of those candidate passages should rank highest. Instead of relying only on contextual relevance, CLEAR uses two signals, including how relevant the passage is to the conversation and how likely the passage is to support the answer.

The answerability signal is learned through entailment distillation. During training, an NLI model evaluates whether passages support known answers, and that supervision is transferred into the reranker. At inference time, CLEAR does not need to know the answer in advance because it has learned to recognize characteristics of answer-supporting passages.

Results and Analysis

We’ll skip the Experimental Setup section and jump straight to the analysis portion.

It’s quite detailed, so feel free to take a look for yourself if interested, but I’d summarize it like this:

One of the more interesting findings is that answerability-aware reranking consistently improved top-ranked precision compared with relevance-only reranking. The gains were especially pronounced when conversations shifted topics, where highly relevant passages were more likely to become distracting rather than answer-supporting.

The researchers’ results suggest that retrieval systems can benefit from distinguishing between content that is merely relevant to a topic and content that actually provides evidence for an answer.

For SEO/GEO strategies, the results reinforce the importance of creating passages that clearly resolve specific information needs, not just broadly cover a subject.

Conclusion

“CLEAR shifts conversational retrieval from aligning to the dialogue context to aligning to the answer the dialogue asks for. The resulting answerability gap is narrowed from two ends of the pipeline: an answerability-aware cross-encoder (A-CE) that distills answer–passage entailment supervision into reranking, and a passage-centric abductive recall channel that brings low-similarity yet answerable passages into the candidate pool. Across TopiOCQA, QReCC, and zero-shot TREC CAsT, the two channels move head-of-ranking metrics together, with the largest gains exactly where topical similarity is the weakest proxy for answerability; applied on top of an LLM rewriter, A-CE still improves it, indicating that answerability supervision is orthogonal to query reformulation.”

Knowing what we do now, why should SEO/GEO professionals care about “From Topical Relevance to Answerability: Entailment Distillation for Conversational Retrieval”?

SEO/GEO professionals should care about this research because it challenges a familiar assumption: content that is highly relevant to a topic is not necessarily the content most useful for answering a user’s question.

The authors show that retrieval systems can favor topically similar passages over passages that actually contain the evidence needed to support an answer, creating what they call an “answerability gap.”

Their results show that modeling answerability improves top-ranked retrieval quality, especially when conversations contain topic shifts or noise. In some cases, relevance-only reranking can even push on-topic but non-answering passages above the passages that actually resolve the question.

For SEO/GEO, the practical implication is that broad topical coverage may not be enough. Content should also contain clear, direct, evidence-bearing passages that can resolve specific information needs.

Caveat: My SEO/GEO takeaway is an application of the paper’s findings; the study itself evaluates conversational retrieval systems, not commercial search or AI visibility platforms.

Outro

I hope you’ve enjoyed this edition of Hamsterdam Research! 🐹

Feel free to comment below or contact me with your feedback.

Stay tuned for another new article, hopefully next week, or check out related research posts below.

Until next time, enjoy the vibes:

Thanks for reading. Happy optimizing!