FRESHLLMS: Refreshing Large Language Models with Search Engine Augmentation (2024)

Abstract: Since most large language models (LLMS) are trained once and never updated, they struggle to dynamically adapt to our ever-changing world. In this work, we present FRESHQA, a dynamic QA benchmark that tests a model’s ability to answer questions that may require reasoning over up-to-date world knowledge. We develop a two-mode human evaluation procedure to measure both correctness and hallucination, which we use to benchmark both closed and open-source LLMS by collecting >50K human judgments. We observe that all LLMS struggle to answer questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked. In response, we develop FRESHPROMPT, a few-shot prompting method that curates and organizes relevant information from a search engine into an LLM’s prompt. Our experiments show that FRESHPROMPT outperforms both competing search engine-augmented prompting methods such as SELF-ASK (Press et al., 2022) as well as commercial systems such as PERPLEXITY.AI. To facilitate future work, we additionally develop FRESHEVAL, a reliable autorater for quick evaluation and comparison on FRESHQA. Our latest results with FRESHEVAL suggest that opensource LLMS such as MIXTRAL (Jiang et al., 2024), when combined with FRESHPROMPT, are competitive with closed-source and commercial systems on search-augmented QA.

heading: 1 Introduction
text: Despite their impressive capabilities, modern LLMS often “hallucinate” plausible but factually incorrect information (Maynez et al., 2020; Liu et al., 2023), which reduces their trustworthiness especially in settings where accurate and up-to-date information is critical. This behavior can be partially attributed to the presence of outdated knowledge
*Work done while at Google.
encoded in their parameters. While additional training using human feedback (Ouyang et al., 2022) or knowledge-enhanced tasks can mitigate this issue, it is not easily scalable for real-time knowledge updates (e.g., stock prices). In-context learning (Brown et al., 2020) is an appealing alternative by which real-time knowledge can be injected into an LLM’s prompt. While recent work has begun to explore augmenting LLM prompts with web search results (Lazaridou et al., 2022; Press et al., 2022), it is unclear how to take full advantage of search engine outputs to increase LLM factuality.
In this work, we collect FRESHQA, a novel benchmark to evaluate the factuality of LLM generations. FRESHQA consists of 600 natural questions that are broadly divided into the four main categories shown in Figure 1. FRESHQA’s questions span a diverse set of topics with diverse difficulty levels (requiring single-hop and multi-hop reasoning), and require a model to “understand” up-to-date world knowledge to be able to answer correctly. Additionally, FRESHQA is dynamic in nature: some of the ground-truth answers may change over time, and a question classified under a specific category may undergo reclassification at some later point in time (e.g., the current false-premise question “How long has Elon Musk been married to his current spouse?” will fall into the fast-changing category if Elon Musk gets married again in the future).
We benchmark a diverse range of both closed and open-source LLMS under a two-mode evaluation procedure: RELAXED, which measures only whether the main answer is correct; and STRICT, which measures whether all of the claims in the response are factual and up-to-date (i.e., no hallucination). Through an extensive human evaluation (> 50K judgements), we shed light on limitations of these models and demonstrate significant room for improvement: for example, all models (regardless of model size) struggle on questions that involve fast-changing knowledge and false premises.
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Motivated by these findings, we develop FRESHPROMPT, a few-shot prompting strategy that takes full advantage of a search engine by integrating upto-date and relevant information into the prompt, including knowledge from related questions asked by other search users. FRESHPROMPT significantly boosts LLM factuality: for example, our best GPT-4 + FRESHPROMPT variant yields an improvement of 32.6% and 49.0% accuracy over the vanilla GPT4 (OpenAI, 2023) on FRESHQA under RELAXED and STRICT, respectively. Further analysis of FRESHPROMPT reveals that both the number of retrieved evidences and their order are key factors behind the correctness of LLM-generated answers.
We make FRESHQA freely available at github.com/freshllms/freshqa and commit to updating the ground-truth answers weekly to encourage exploration of methods to improve LLM factuality. To facilitate future work, we develop FRESHEVAL, an LLM-based autorater that reliably replicates human judgments of model responses (> 96% average agreement with human annotators). Intriguingly, our latest evaluation with FRESHEVAL reveals that open-source LLMS such as MIXTRAL, when paired with FRESHPROMPT, rival the performance of closed-source and commercial systems on search-augmented QA (MIXTRAL 8X7B + FRESHPROMPT obtains a 11% absolute accuracy improvement over PERPLEXITY.AI’s ONLINE LLM 70B), highlighting the potential of accessible AI solutions.

heading: 2 FreshQA
text: To address LLM factuality assessment, we build FRESHQA, a dynamic benchmark with 600 questions covering diverse question and answer types. We collected FRESHQA by recruiting both NLP researchers1 and online freelancers2 to write questions of varying difficulty and topics (arts, music, politics, government, religion, science and technology, environment, transportation, sports, etc.), focusing on questions whose answers evolve over time. Annotators were shown exemplars of the four broad question types (see Figure 1) and asked to write questions at two difficulty levels: one-hop, where the question directly states all information needed for the answer (e.g., “Who is the CEO of Tesla”); and multi-hop, where the question requires additional reasoning steps to find the answer (e.g., “What country does the Wimbledon women’s champion play for?”). Annotators were encouraged to write questions that involve fresh knowledge and appear natural as search engine queries. For falsepremise questions, we requested a brief explanation elucidating why the question is flawed.3
Quality control: Upon obtaining the initial dataset, we performed rigorous data cleaning and quality checks. This included manual review for
1including the authors and their colleagues 2We use UPWORK (https://www.upwork.com) with a compensation rate of $2 per example. 3Additionally, annotators were asked to include the year the answer last changed and a supporting URL.
well-formed questions, removal of duplicates and invalid questions (e.g., too easy or controversial), and verification of answers and supporting URLS.4
Data splits: FRESHQA is divided into a test set with 500 examples (125 per question type), a development set with 100 examples (25 per question type), and a 15-example demonstration set for fewshot learning. The development set is reserved for future use and was not used in this paper.5

heading: 2.1 Evaluation
text: Model responses were evaluated by two authors in a two-mode evaluation procedure: RELAXED, which assesses the correctness of the main answer; and STRICT, which additionally examines whether all of the facts in the answer are accurate (i.e., no hallucination). This approach provides both ends of the spectrum for evaluating factuality with the difference between a model’s STRICT and RELAXED performance indicating the degree of hallucination. In both modes, the primary answer must be correct and either definitively stated or obviously inferable. Any additional information must not contradict the primary answer. For false-premise questions, the model must identify false premises to get credit. Our protocol also considers many other edge cases (e.g., approximate numbers, ungrammatical answers), which are fully detailed in Appendix A. Here, we provide an illustrative example:
Question: Who won the biggest singletournament payday in tennis history? Gold answer: Novak Djokovic Model response: The biggest single-tournament payday in tennis history was won by Novak Djokovic at the 2021 Australian Open.
The model receives credit under RELAXED for a correct primary answer; however, the tournament in question was actually the 2022 ATP Finals, so the answer is judged as incorrect under STRICT.
Inter-rater agreement: Two authors independently evaluated a subset of 100 model responses in both modes and had an agreement of 99% for RELAXED and 96% for STRICT, validating the reliability of our evaluation protocol.
4For each question, we also manually collected additional valid answers (e.g., different names of the same person) and included the expected next review date. To facilitate future answer updates, we excluded questions whose answers are likely to change more frequently than once per week.
5Although our data splits are initially balanced across question types, the distribution may change over time due to reclassification of questions from one category to another.
FRESHEVAL: Human evaluation of LLMgenerated answers can be extremely timeconsuming, especially for long responses in the STRICT setting. To facilitate future evaluation and comparison, we develop FRESHEVAL, a simple autorater that uses few-shot in-context learning to teach an LLM to evaluate correctness and hallucination in LLM-generated responses given the questions and their valid answers, achieving an average agreement of 96.5% with human judgments for RELAXED and 96% for STRICT. We use FRESHEVAL in Section 5 to evaluate the latest LLMS; all other results in this paper are from human judgments. See Appendix B for more details about FRESHEVAL.

heading: 3 Offline LLMS struggle on FRESHQA
text: We first use FRESHQA to benchmark LLMS without access to real-time data or web browsing capabilities.6 We simply feed individual questions into each model and use greedy decoding. While all LLMS (regardless of size) predictably struggle on questions requiring up-to-date knowledge, they also underperform on false-premise questions.
Baselines: We evaluate a series of models: T5 (Raffel et al., 2020; Lester et al., 2021), PALM and PALMCHILLA (Chowdhery et al., 2022) (varying in size from 770M to 540B parameters), optionally using FEW-SHOT prompting (Brown et al., 2020) and Chain-of-Thought (COT) prompting (Wei et al., 2022); FLAN-T5 and FLANPALM (Chung et al., 2022; Longpre et al., 2023), GPT-3.5 (Ouyang et al., 2022), CODEX (Chen et al., 2021a), CHATGPT, and GPT-4.7 See Appendix C for more details.

heading: 3.1 Results and Discussion
text: FRESHQA presents a challenge for LLMS: Figure 2 shows the accuracy of different LLMS on FRESHQA (see Appendix E for concrete numbers). All models struggle on FRESHQA, with overall accuracy ranging from 0.8% to 32.0% under STRICT, and
6Note that even without access to up-to-date information, a model can still give accurate answers to some current questions by making random guesses or using past valid responses. For example, for the question “Which drama series won the most recent Primetime Emmy Award for Outstanding Drama Series?”, a model trained in 2020 might correctly answer “Succession” (as of this writing) since it won in 2020 and the two most recent years.
7All models were benchmarked in 2023. Both CHATGPT (built upon GPT-3.5) and GPT-4 had access to the current date. We note that CHATGPT now has browsing capabilities.
0.8% to 46.4% under RELAXED. There is a marked decrease in accuracy for CHATGPT and GPT-4 when switching from RELAXED to STRICT due to the lack of access to up-to-date information, often resulting in “outdated” answers (starting with “As of my knowledge cutoff date”) or refusal to answer (e.g., “I cannot provide real-time information”). PALM’s accuracy also drops significantly under STRICT, primarily due to ill-formed responses (conversationlike responses with unexpected end-of-turn [eot] tokens) and hallucination. In contrast, FLAN-PALM and CODEX show minimal hallucination due to their concise and direct answers.
LLMS struggle with questions involving up-todate information and false premises: Outdated knowledge drastically lowers model accuracies on questions about fast-changing or recent knowledge. While GPT-4 generally obtains the highest accuracy on these questions, it never exceeds 15% in both evaluation modes. Our evaluation shows that CHATGPT and GPT-4 have been exposed to data containing information beyond their knowledge cutoff (see Appendix F). GPT-4 is more reluctant to answer fast-changing questions (refusing to answer 60% of the time) compared to CHATGPT (16%).
Questions with false premises also pose a hurdle for LLMS. Larger models do not improve accuracy for T5 and PALM (“flat scaling”), with performance ranging from 0.0% to 1.6%. However, GPT-3.5, CHATGPT, and GPT-4 outperform other models significantly, achieving accuracies between 25.8% to 42.7% under STRICT and 32.3% to 66.9% under RELAXED. Our findings suggest that these models may have been trained to handle false-premise queries.
COT increases hallucination and multi-hop reasoning is challenging for LLMS: Overall, FEW-
SHOT and COT prompting confer benefits to large and moderately-sized models on questions with valid premises about never-changing or old knowledge. Under STRICT, FEW-SHOT and COT yields +36.1% and +26.9% accuracy improvement, respectively, over zero-shot prompting with PALM 540B on questions about pre-2022 knowledge (+21.9% and +29.7% under RELAXED). COT generally outperforms FEW-SHOT under RELAXED, whereas FEW-SHOT performs better under STRICT, as COT introduces more room for hallucination.
Most LLMS have difficulty with multi-hop questions. T5 LARGE and XL are incapable of dealing with this type of questions, while FLAN-PALM 540B, CODEX, and GPT-3.5 suffer the most when switching from one-hop to multi-hop questions. GPT-4, on the other hand, maintains stability across both types of questions, with less than a 2% difference in accuracy. See Appendix E for details.

heading: 4 Prompting Search Engine-Augmented Language Models
text: The low accuracies in Section 3 are expected, as none of the evaluated models had access to real-time information. Here, we evaluate the impact of search engine augmentation to LLMS on FRESHQA. We present FRESHPROMPT, a simple fewshot prompting method that substantially boosts LLM factuality by incorporating up-to-date information from a search engine into the prompt.

heading: 4.1 FRESHPROMPT
text: FRESHPROMPT uses a text prompt to (1) incorporate contextually relevant and current information (including answers to relevant questions) from a search engine into a model, and (2) teach the model to reason over retrieved evidences. More specifi-
cally, given a question q, we first use q verbatim to query a search engine, i.e., GOOGLE SEARCH8, and retrieve all search results, including the answer box, organic results, and other useful information, such as the knowledge graph, questions and answers from crowdsourced QA platforms, and related questions from search users (see Figure 9 in Appendix G). For each result, we extract the associated text snippet x along with details, such as source s (e.g., WIKIPEDIA), date d, title t, highlighted words h. These snippets are standardized and then organized into a list of k retrieved evidences E = {(s, d, t, x, h)} (Figure 3, left). To prioritize recent evidences, we arrange the evidences E in the prompt from oldest to newest.
To guide the model in learning the task, we provide a few input-output exemplars at the start of the prompt. Each demonstration includes an example question and a list of retrieved evidences, followed by a chain-of-thought reasoning to derive the most relevant and current answer (Figure 3, right). While we include some examples with false premises, we also test an explicit false premise check in the prompt: “Please check if the question contains a valid premise before answering”. Figure 10 in Appendix H shows a realistic prompt.

heading: 4.2 Experiment setup
text: We closely follow the setup in Section 3, except for cases where we lack control over the model’s decoding via an API (e.g., PERPLEXITY.AI).9 In addition to GPT-3.5 and GPT-4, we evaluate GOOGLE SEARCH10; PERPLEXITY.AI (PPLX.AI), which combines an LLM and a search engine to respond to
8We scrape the results from GOOGLE SEARCH using SERPAPI (https://serpapi.com).
9All models were benchmarked in 2023. We note that some of the evaluated models may have evolved, posing a challenge to result reproducibility.
10We simply query GOOGLE SEARCH and use the answer in the answer box (if any) or the text snippet of the top-1 search result.
users’ queries;11 and SELF-ASK (Press et al., 2022), which uses few-shot in-context learning to teach an LLM to decompose a question into simpler subquestions that are answered via GOOGLE SEARCH.12
FRESHPROMPT setup: We employ FRESHPROMPT for both GPT-3.5 and GPT-4, sequentially adding the following retrieved evidences to the input prompt: o organic search results, r related questions from search users, a questions and answers from crowdsourced platforms, and the snippets from the knowledge graph and answer box (if available). Due to context limits, we retain the top n evidences (closest to the end of the prompt) sorted by date. Default values are (o, r,a,n,m) = (10, 2, 2, 5) for GPT3.5, and (o, r,a,n,m) = (10, 3, 3, 10) for GPT-4. Additionally, we include m = 5 question-answer demonstrations at the start of the prompt.

heading: 4.3 Results and Discussion
text: FRESHPROMPT significantly boosts FRESHQA accuracy: Table 1 shows our results under STRICT (see Appendix I for RELAXED). FRESHPROMPT offers large improvements over vanilla GPT-3.5 and GPT-4 across the board. GPT-4 + FRESHPROMPT achieves absolute accuracy improvements of 47% and 31.4% over GPT-4 under STRICT and RELAXED, respectively. The absolute accuracy gap between STRICT and RELAXED diminishes substantially with FRESHPROMPT (from 17.8% to 2.2%), indicating a significant reduction in outdated and hallucinated answers. The most significant improvements for both models occur in categories related to recent knowledge, including fast-changing and slow-changing questions. However, even questions pertaining to older
11https://www.perplexity.ai. At the time of evaluation, PPLX.AI was a combination of GPT-3.5 and BING SEARCH, and was able to provide both concise and detailed answers. We evaluated its concise answers.
12We used the few-shot prompt provided by SELF-ASK’s authors and applied it to both GPT-3.5 and GPT-4. For simplicity, we evaluated solely the final answer from SELF-ASK, disregarding intermediate answers.
knowledge benefit from FRESHPROMPT (+30.5% and +9.9% improvements for GPT-4 under STRICT and RELAXED, respectively, on questions with valid premises about pre-2022 knowledge). Furthermore, FRESHPROMPT yields notable accuracy gains on false-premise questions (+37.1% and +8.1% respective improvements under STRICT and RELAXED).
FRESHPROMPT outperforms other searchaugmented methods by a large margin: GPT-4 + FRESHPROMPT surpasses all other methods by a substantial margin, with its best variant (15 retrieved evidences per question) achieving 77.6% and 79.0% overall accuracies under STRICT and RELAXED, respectively. Compared to PPLX.AI and SELF-ASK (all built on top of GPT-3.5), GPT-3.5 + FRESHPROMPT demonstrates a respective increase of +3.8% and +14.4% in overall accuracy under STRICT. However, under RELAXED, PPLX.AI outperforms GPT-3.5 + FRESHPROMPT by +4.2%, mainly due to its higher accuracy on false-premise questions (58.1% vs. 41.1%). The significant 14.0% accuracy gap between STRICT and RELAXED for PPLX.AI indicates a considerable amount of hallucination in its outputs. Overall, all
search-engine augmented approaches (SELF-ASK, PPLX.AI, and FRESHPROMPT) provide substantial improvements over vanilla GPT-3.5 and GPT-4.
Premise check improves accuracy on falsepremise questions but can hurt accuracy on valid premise questions: Our findings indicate that GPT-3.5, GPT-4, and PPLX.AI, are likely trained to address false-premise queries. Additionally, we empirically find that several LLMS can debunk falsepremise questions if explicitly prompted: “Please check if the question contains a valid premise before answering”, resulting in significant accuracy improvements on false-premise questions. For example, adding this premise check boosts accuracy by +23.4% and +6.4% for GPT-3.5 and GPT-4, respectively, under STRICT (+22.6% and +11.3% under RELAXED). However, this is detrimental for GPT-3.5 across other question types, reducing overall accuracy by 20.8% and 21% under STRICT and RELAXED, respectively. Conversely, GPT-4 experiences minimal impact, with only a 0.6% decrease under STRICT and a 1.2% increase under RELAXED.
Including more relevant and up-to-date evidences at the end of the input context is bene-
ficial: We also analyze how the order of the evidences in the prompt impacts GPT-4’s accuracy. Our results show that using the order returned by GOOGLE SEARCH (SEARCH ORDER, top search results at the end of the input context) or sorting the evidences by their associated date information (TIME ORDER, more recent results at the end) generally results in better accuracy compared to using a random order (RANDOM ORDER), with up to a +2.2% higher overall accuracy in both evaluation modes. However, using only the text snippet for each evidence without additional information (e.g., source, date) as in GPT-4 + FRESHPROMPT slightly decreases accuracy, with less than 1% in both evaluation modes.
Additional retrieved information beyond organic search results provides further gains: Incorporating additional retrieved evidences beyond organic search results, such as answer boxes and related questions from search users, enhances performance. Removing answer boxes decreases GPT-4 + FRESHPROMPT’s overall accuracy by 1.4% under STRICT (1.6% under RELAXED). Removing both answer boxes and other relevant information (including related questions) reduces GPT-4 + FRESHPROMPT’s overall accuracy by 3.2% under STRICT (3.0% under RELAXED).
Increasing the number of retrieved evidences enhances FRESHPROMPT’s effectiveness: We explore the effect of the number of retrieved evidences per question as well as the number of demonstrations by varying these numbers for GPT-4 + FRESHPROMPT. By default, we use 10 retrieved evidences for each question and 5 demonstrations. Our results reveal that the number of retrieved evidences
per question is the most important ingredient for achieving highest accuracy. Under STRICT, increasing this number from 1 to 5, 10, and 15 results in respective accuracy improvements of of +9.2%, +14.2%, and +16.2%. This indicates GPT-4’s adeptness in accommodating an increasing number of retrieved evidences, including conflicting answers, to provide responses grounded in the most factual and current information. Conversely, increasing the number of demonstrations from 5 to 15 marginally decreases accuracy in both evaluation modes, with a 1% overall decrease under STRICT.
Verbose demonstrations help with complex questions but also amplify hallucinations: To evaluate the effect of the writing style of the answer (including the reasoning) in each demonstration, we manually rewrite these answers into a more verbose version (LONG DEMONSTRATION ANSWERS). Our manual inspection reveals that while verbose answers may aid in tackling intricate questions, they can also be detrimental by allowing for hallucination, leading to a 2.6% decrease in overall accuracy under STRICT.

heading: 5 An updated evaluation in 2024
text: While our previous experiments were conducted in 2023, we use FRESHEVAL to evaluate new LLMS on an updated version of FRESHQA (February 5, 2024). We evaluate GPT-4, MIXTRAL 8X7B, LLAMA2 (Touvron et al., 2021), PPLX.AI’s ONLINE LLM, and YOU.COM’s WEB LLM on the same date of February 5, 2024. See Appendix D for more details.
As shown in Table 2, even recent LLMS, with or without search engine augmentation, struggle with
questions requiring up-to-date knowledge (with accuracies ranging from 6.1% to 44.9% on questions about post-2024 knowledge) as well as questions with false premises (accuracies ranging from 33.1% to 68.5%). Strikingly, we discover that open-source LLMS such as MIXTRAL 8X7B, when paired with FRESHPROMPT, are competitive with closed-source and commercial systems on FRESHQA. For example, MIXTRAL 8X7B + FRESHPROMPT obtains a 11% absolute accuracy improvement over PPLX.AI’s ONLINE LLM 70B. FRESHPROMPT narrows the performance gap between GPT-4 and MIXTRAL 8X7B to under 9% in all question categories, with the largest gap on slow-changing questions decreasing from 34.4% to 8.8%.

heading: 6 Related Work
text: Knowledge augmented LLMS: Many prior works study semi-parametric knowledge augmentation in LLMS via additional fine-tuning (Guu et al., 2020; Lewis et al., 2020; Borgeaud et al., 2022; Izacard et al., 2022), while others advocate for knowledge generation instead of retrieval (Yu et al., 2023a; Sun et al., 2023). FRESHPROMPT aligns with the recent trend of retrieval-augmented generation (Nakano et al., 2021; Lazaridou et al., 2022; Menick et al., 2022; Yao et al., 2022; Press et al., 2022; Khattab et al., 2022; Schick et al., 2023; Luo et al., 2023). Similar to our method, Lazaridou et al. (2022) employ a few-shot prompting approach that inserts documents from GOOGLE SEARCH into LLM prompts. We refrain from comparing to their method due to its expensive inference cost, as they chunk retrieved documents into evidence paragraphs and perform k = 50 inference calls to the LLM to generate k answers followed by LLM reranking. In contrast, FRESHPROMPT only performs a single inference call to the LLM. SELF-ASK (Press et al., 2022) also uses few-shot in-context learning to teach an LLM to ask itself follow-up questions before answering the initial question, although it focuses more on decomposition.
Time-sensitive QA: FRESHQA fits into a growing body of work benchmarking LLMS’ temporal reasoning capabilities (Chen et al., 2021b; Zhang and Choi, 2021; Liska et al., 2022; Kasai et al., 2022). Chen et al. (2021b) created TIMEQA by extracting evolving facts from WIKIDATA and then synthesizing timestamped question-answer pairs. Zhang and Choi (2021) constructed SITUATEDQA by annotating realistic questions from existing open-
domain QA datasets with temporal context (i.e., timestamps). STREAMINGQA (Liska et al., 2022) consists of both LLM-generated and human-written questions, all answerable using a corpus of timestamped news articles. Also related is the dynamic REALTIMEQA benchmark (Kasai et al., 2022), which evaluates models weekly on multiple-choice questions about new events extracted from news websites. In contrast, FRESHQA contains a fixed set of human-written open-ended questions whose answers by nature can change based on new developments in the world and thus offers a complementary generative evaluation of time-sensitive QA.
QA over questionable or counterfactual premises: Recent work has also introduced QA benchmarks with questionable premises (Yu et al., 2023c; Kim et al., 2023) or counterfactual premises (Yu et al., 2023b). CREPE (Yu et al., 2023c) includes Reddit questions with false premises annotated by human workers. Kim et al. (2023) constructed (QA)2 using frequently searched queries annotated by expert annotators and crowdworkers, distinguishing between those with and without questionable premises. Consistent with these efforts, we find that current LLMS struggle with false premise questions; additionally, several LLMS can debunk a false-premise question if explicitly asked to check for the premise’s validity. Similar to above, these benchmarks are complementary and combining them is a promising direction for future work.

heading: 7 Conclusion
text: Our work offers a fine-grained and exhaustive evaluation of the capabilities of modern LLMS to adapt to ever-changing world knowledge with and without search engine augmentation. In the process, we develop a new dataset—FRESHQA—of 600 questions that test a broad range of reasoning abilities, from the incorporation of fast-changing knowledge to identification of questions with false premises. Our two-mode evaluation also provides a way to measure both correctness and hallucination. Additionally, we propose a simple few-shot in-context learning algorithm called FRESHPROMPT that incorporates relevant evidences retrieved from a search engine into an LLM’s prompt. FRESHPROMPT significantly improves performance over competing search engine-augmented approaches on FRESHQA, and an ablation reveals that factors such as the number of incorporated evidences and their order
impact the correctness of LLM-generated answers. We release FRESHQA and commit to updating its answers regularly to facilitate future research. Additionally, we develop FRESHEVAL, a reliable autorater for quick evaluation and comparison on FRESHQA.

heading: 8 Limitations and Future Work
text: One obvious challenge with FRESHQA is the need for regular answer updating by the maintainers; in the interim period between updates, the answers to some questions might become stale. This could be addressed by support from the open-source community. On the method side, FRESHPROMPT only performs one search query per question, and thus it can be further improved via question decomposition and multiple search queries (Khattab et al., 2022). Since FRESHQA consists of relatively simple questions, it is also unclear how well FRESHPROMPT performs in the context of long-form QA (Fan et al., 2019). Finally, FRESHPROMPT relies on in-context learning and thus may underperform approaches that fine-tune the base LLM on new knowledge.

heading: 9 Acknowledgements
text: We thank Colin Raffel, Hamed Zamani, and Subhransu Maji for helpful discussion and feedback. We would also like to thank Chengrun Yang, Xinyun Chen for their insightful comments on this manuscript. Finally, we are grateful to the following people for their contributions to creating our FRESHQA dataset: Marzena Karpinska, Dustin Tran, Daniel Cer, Sam Fullerton, Elizabeth Clark, Nishant Raj, Xiaoyu Song, Yapei Chang, Yixiao Song, Nader Akoury, Ankita Gupta, Bill Ray, Chau Pham, Wenlong Zhao, Maximilian Mozes, Simeng Sun, Ronan Salz, Kalpesh Krishna, Katherine Thai, Kanishka Misra, Salaheddin Alzu’bi, Erica Cai, Thibault Sellam, Jiao Sun, Dhruv Agarwal, Tessa Masis, Andrew Drozdov, Brian Lester, George Wei, Naveen Jafer Nizar, Shufan Wang, Youngwoo Kim, and Shib Sankar Dasgupta. This project was partially supported by award IIS-2046248 from the National Science Foundation (NSF), as well as NSF’s CLOUDBANK program.

heading: A Evaluation protocol
text: Figure 4 shows specific examples of each evaluation criteria.
B Inter-rater agreement and automatic evaluation
Two authors independently evaluated a randomly sampled subset of 100 answers across models (including 50 questions with valid premises and 50 questions with false premises) in both modes RELAXED and STRICT.
To facilitate future evaluations, we also develop FRESHEVAL, an autorater that uses few-shot incontext learning to teach an LLM to judge model responses. In each evaluation, the model is conditioned on a given question, a list of valid answers, and a model response, and is then expected to generate a comment on the correctness of the response, followed by a final judgement. At the start of each input prompt, we also provide an instruction of the evaluation task, and sample comments and evaluations of the examples in Figure 4 as demonstrations.13 See Figure 5 and Figure 6 for FRESHEVAL’s prompts for RELAXED and STRICT evaluations, and Figure 7 for FRESHEVAL’s sample output for STRICT evaluation.
Table 3 reports the inter-rater agreement between the two human raters, and between FRESHEVAL and each human rater, in terms of exact accuracy. The two human raters had an agreement of 99% for RELAXED and 96% for STRICT, while FRESHEVAL achieved an average agreement of 96.5% with human evaluations for RELAXED and 96% for STRICT. Overall, the high accuracies demonstrate that our evaluation protocol is reproducible and reliable, and FRESHEVAL can be used in place of human evaluation on FRESHQA.

heading: C Additional experiment setup details for Section 3
text: To increase reproducibility, we used greedy decoding (with a temperature of 0), which selects the most likely token at every decoding timestep, and a
13In our experiments, we found that using separate prompts for RELAXED and STRICT evaluations resulted in better performance compared to using a single, combined prompt for both evaluation modes. We also found that additionally incorporating retrieved evidences for the question into the prompt did not improve inter-rater agreement between FRESHEVAL and human raters.
maximum number of 256 generated tokens for all models. Note that the API for some models is nondeterministic by default, even with a temperature of 0.
For non-chat models that were not pre-trained with a QA task, we fed them a text prompt of the format: “Q: <question>\nA: ” (“\ n” is the new line character). For few-shot in-context learning with T5 and PALM, we used the 5-shot demonstrations for TRIVIAQA (Joshi et al., 2017) used in Sun et al. (2023).
For OPENAI models, we used the 2023-03-15-preview API in AZURE OPENAI SERVICE with the model names text-davinci-003, code-davinci-002, gpt-3.5-turbo, and gpt-4 for GPT-3.5, CODEX, CHATGPT, and GPT-4, respectively.

heading: D Additional experiment setup details for Section 5
text: For the evaluated models, we used OPENAI API for GPT-4 (gpt-4-0125-preview), TOGETHER API for MIXTRAL (Mixtral-8x7B-Instruct-v0.1) and LLAMA-2 (Llama-2-70b-chat-hf), PPLX-API for PPLX.AI’s ONLINE LLM (pplx-70b-online), and YOU API for YOU.COM’s WEB LLM.
For the base LLM of FRESHEVAL, we used GPT-4 (gpt-4-1106-preview).

heading: E Additional experiment results for
text: Section 3
Table 4 and Table 5 show the accuracy of different LLMS on FRESHQA under STRICT (no hallucination) and RELAXED evaluations, respectively.

heading: F CHATGPT/GPT-4’s awareness of recent knowledge
text: Our manual evaluation suggests that CHATGPT and GPT-4 have been exposed to data containing information beyond their knowledge cutoff date (see Figure 8).

heading: G GOOGLE SEARCH results
text: Figure 9 shows different types of search results from GOOGLE SEARCH for given a query.

heading: H A realistic prompt for FreshPrompt
text: Figure 10 displays a realistic prompt for FRESHPROMPT.

heading: I Additional experiment results for
text: Section 4
Table 6 presents the accuracy of different search engine-augmented LLMS on FRESHQA under RELAXED.

heading: Future Work
text: To facilitate future evaluations, we also develop FRESHEVAL, an autorater that uses few-shot incontext learning to teach an LLM to judge model responses. In each evaluation, the model is conditioned on a given question, a list of valid answers, and a model response, and is then expected to generate a comment on the correctness of the response, followed by a final judgement. At the start of each input prompt, we also provide an instruction of the evaluation task, and sample comments and evaluations of the examples in Figure 4 as demonstrations. 13 See Figure 5 and Figure 6 for FRESHEVAL’s prompts for RELAXED and STRICT evaluations, and Figure 7 for FRESHEVAL’s sample output for STRICT evaluation. Table 3 reports the inter-rater agreement between the two human raters, and between FRESHEVAL and each human rater, in terms of exact accuracy. The two human raters had an agreement of 99% for RELAXED and 96% for STRICT, while FRESHEVAL achieved an average agreement of 96. 5% with human evaluations for RELAXED and 96% for STRICT. Overall, the high accuracies demonstrate that our evaluation protocol is reproducible and reliable, and FRESHEVAL can be used in place of human evaluation on FRESHQA