English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

While chain-of-thought (CoT) prompting has revolutionized how LLMs performreasoning tasks, its current methods and variations (e.g, Self-consistency,ReACT, Reflexion, Tree-of-Thoughts (ToT), Cumulative Reasoning (CR)) sufferfrom limitations like slowness, limited context grounding, hallucination andinconsistent outputs. To overcome these challenges, we introduce Evidence toGenerate (E2G), a novel single-agent, two-step prompting framework. Instead ofunverified reasoning claims, this innovative approach leverages the power of"evidence for decision making" by first focusing exclusively on the thoughtsequences (the series of intermediate steps) explicitly mentioned in thecontext which then serve as extracted evidence, guiding the LLM's outputgeneration process with greater precision and efficiency. This simple yetpowerful approach unlocks the true potential of chain-of-thought likeprompting, paving the way for faster, more reliable, and more contextuallyaware reasoning in LLMs. \tool achieves remarkable results robustly across awide range of knowledge-intensive reasoning and generation tasks, surpassingbaseline approaches with state-of-the-art LLMs. For example, (i) on LogiQAbenchmark using GPT-4 as backbone model, \tool achieves a new state-of-theAccuracy of 53.8% exceeding CoT by 18%, ToT by 11%, CR by 9% (ii) a variant ofE2G with PaLM2 outperforms the variable-shot performance of Gemini Ultra by 0.9F1 points, reaching an F1 score of 83.3 on a subset of DROP.

Evidence to Generate (E2G): A Single-agent Two-step Prompting for Context Grounded and Retrieval Augmented Reasoning